Category: 3. Business

Introduction

Background

The World Health Organization emphasizes that digital technologies are essential components and enablers of sustainable health systems and universal health coverage []. Despite robust support and the rapid expansion of eHealth services in many countries [], a significant digital divide remains [].

Marginalized and vulnerable populations—such as older adults, individuals with limited education, low-income groups, and rural residents—face substantial challenges in accessing and using eHealth services [,]. These challenges include acquiring necessary internet-enabled devices, developing sufficient internet usage skills, and improving digital literacy [,]. Such barriers increase their risks of inadequate internet access and poor eHealth literacy, further exacerbating the digital divide they experience [-]. This divide in eHealth utilization between users and nonusers undermines the potential benefits of eHealth services, limiting their capacity to contribute effectively to improved health outcomes [].

On the basis of consumer purchasing decision model or new techniques of adoption decision-making [,], individuals’ engagements with eHealth services follow a hierarchical progression through 3 stages: awareness, want, and adoption []. Adequate knowledge of emerging technologies fosters a more rational want for eHealth services []. For example, a study conducted in a Spanish border community revealed that eHealth literacy positively influences the intention to use telehealth services []. However, it is important to note that a need for eHealth does not always lead to actual adoption. As health status deteriorates and physical functionality declines, older adults often exhibit a heightened demand for eHealth services, particularly those that help overcome geographical constraints []. However, because of vision impairments and reduced learning capacities [], their actual utilization of eHealth remains significantly lower than that of younger individuals []. Nevertheless, once older adults begin using eHealth services, they tend to show greater persistence in using health management apps compared to their younger counterparts [].

According to their functions of eHealth services, eHealth services’ typical classifications include health information services [], e-consulting services [], online appointment booking [], and eHealth commerce []. Health information services, which involve activities such as health information seeking, health risk assessments, and personal health records, are prevalently used globally [-]. E-Consulting, introduced in the early 2000s [,], has seen significant adoption in countries such as England and Canada [-], although its uptake has been slower in low- and middle-income countries []. Online appointment booking, including online scheduling and access to test and laboratory results [], has been implemented in many countries, including England, Australia, and Canada [-]. eHealth commerce presents a promising opportunity to expand access to medications and has been implemented in many countries []. However, significant disparities exist in the formats and complexity of these eHealth services [,,], which may result in varying levels of awareness, want, and adoption among individuals across different eHealth service categories [-]. Unfortunately, there is a lack of studies that specifically examine the awareness of, want for, and adoption of eHealth services within these distinct categories.

Study Objectives

This study specifically focuses on hospitalized patients. Hospitalized patients often encounter challenges that exacerbate their exposure to a deeper digital divide and increase their demands for eHealth services [,]. These challenges include mobility impairments [], prolonged waiting times [], and additional barriers to follow-up care and chronic disease management after discharge [-]. The heightened demand for eHealth services among inpatients highlights their importance as a key target group for such interventions. Addressing the digital divide in eHealth services for this population is critical, as it may provide valuable insights for other countries in developing targeted strategies to promote eHealth adoption. Therefore, this study aimed to analyze the digital divide in awareness of, want for, and adoption of information-based, treatment intermediary, and treatment eHealth services using the awareness, want, and adoption (AWAG) segment matrix. In addition, it seeks to explore the underlying factors contributing to these disparities.

Methods

Clinical Context

In China, grade A tertiary hospitals (commonly referred to as Sanjia hospitals) represent the highest level of medical institutions, with responsibilities that encompass the provision of specialized health care, the advancement of medical education, and the conduct of advanced research []. Considering the differences in models of eHealth services, the hierarchy, and representativeness of patient sources, this study purposively selected 2 grade A tertiary hospitals and 1 tertiary hospital in Jinan, recognized for offering eHealth services, as the sampling sites. These 3 general hospitals represent distinct tiers of tertiary health care institutions, including national-level hospitals, provincial hospitals, and municipal hospitals. They serve diverse patient populations and reflect varying scales of hospital organization. Each facility is equipped with comprehensive inpatient departments and exhibits unique characteristics in the development of eHealth [-], as detailed in .

Study Design and Data Collection

In this study, the sample size calculation accounted for the issue of multiple comparisons. To address this, the significance level (α=.0167, 0.05/3) was adjusted using multiple Bonferroni corrections. The calculation was performed using the following standard formula, indicating that a minimum of 895 participants was needed. Considering an anticipated 30% nonresponse rate, the final required sample size was adjusted to 1279 participants.

$n=\frac{{\mathrm{Z}}_{1–\mathrm{\alpha }/2}^2\times p(1–p)}{{\mathrm{\delta }}^2}$

where $Z_{1–\alpha /2}$= 2.393, $p$ = 0.5, and $\mathrm{\delta }$= 0.04.

This study used a multistage stratified sampling approach to select participants from 3 participating hospitals. First, the sample size for each hospital was allocated proportionally based on the number of beds in each respective hospital. Subsequently, inpatients were randomly selected from all departments, excluding the emergency and obstetric departments. Specifically, in hospital A, 587 inpatients were selected from 17 departments. In hospital B, 207 inpatients were randomly chosen across 15 departments. In hospital C, 484 inpatients were selected from 15 departments. All wards within each department were included in the investigation, and a total of 305, 104, and 268 wards were included from hospitals A, B, and C, respectively. Two bed numbers were randomly selected using a random number method, and the patients occupying the corresponding beds in each ward were systematically surveyed. If the invited patient declined, the patient in the adjacent bed was approached as a replacement. As a result, a total of 1354 inpatients participated in this survey.

A face-to-face questionnaire survey was conducted across inpatient departments in these hospitals from June to October 2023. The investigators are interns majoring in preventive medicine, who have a relatively high level of medical literacy. To ensure the data quality, a comprehensive training program was implemented to clarify the questionnaire’s content and establish standardized criteria for questioning before conducting the survey. All respondents completed the questionnaire face-to-face with trained investigators, after providing their informed consent and signing the questionnaire.

This survey recruited inpatients based on the following inclusion criteria: (1) aged ≥15 years; (2) able to communicate effectively and complete questionnaires independently or with assistance; (3) not hospitalized due to childbirth or accidental injuries; (4) no history of major mental illness, language impairment, or cognitive impairment; and (5) provided informed consent. After excluding samples with missing key information or those that did not meet the inclusion criteria, a total of 1322 inpatients from 3 hospitals were included as survey participants.

Ethical Considerations

The study was approved by the Ethics Committee of the School of Public Health, Shandong University, P.R. China (LL20230602). Participants who provided written informed consent were included in the study. Data were collected anonymously by the research team and stored securely in locked files. Participation was entirely voluntary, and no compensation was offered for participation.

Measures

Measures of Dependent Variables

This study examined 12 eHealth services based on the established definition and scope of eHealth []. Existing literature indicates that eHealth can produce 3 primary effects.

1. The signaling effect: The internet enables patients to access and evaluate information about health care services, providers, and their quality at a reduced cost []. This helps mitigate information asymmetry in health care, which, in turn, enhances individuals’ health literacy, improves patient-provider matching efficiency, and elevates the overall quality of care [-].
2. The intermediary effect: eHealth facilitates the use of various nondiagnostic and nontreatment resources, such as online triage and appointment scheduling for examinations and surgeries. These services enhance both the accessibility and equity of high-quality medical resources by leveraging eHealth’s intermediary role [-].
3. The substitution effect: a range of medical services, including online consultations, telemedicine, and follow-up care, expands the allocation of health care resources through the application of information technology. By substituting traditional in-person services, eHealth improves the fairness and accessibility of high-quality medical care [,].

On the basis of these 3 effects and the stages of the patient journey [], these 12 services were categorized into 3 groups: information-based services, treatment intermediary services, and treatment services, as detailed in . Information-based services provide health and medical information aimed at enhancing health literacy while reducing costs [,]. Treatment intermediary services support the preadmission process by leveraging nonmedical resources, thereby improving accessibility and the efficiency of health care delivery [,]. Treatment services, on the other hand, use information technology to optimize the allocation of medical resources, complement traditional health care practices, and promote equitable access to high-quality medical services [-].

In this study, the awareness, want for, and adoption of eHealth services were treated as dependent variables. Inpatients were asked whether they had heard of any eHealth services (1=no, 2=yes). The awareness of eHealth services was categorized as 1 if the inpatient had heard of them and 0 otherwise. For patients who had not heard of eHealth services, the investigator provided a brief explanation of their purpose and functionality. Following this explanation, patients were asked about their intention to use eHealth services (1=no, 2=yes). Want for eHealth was then categorized as 1 if the patient expressed a willingness to use them and 0 otherwise. In addition, inpatients were asked about their experience with using eHealth services (1=no, 2=used with the help of family members, 3=used independently). Adoption of eHealth services was categorized as 1 if the inpatient reported using them independently or with the help of family members and 0 otherwise.

Measures of Independent Variables

On the basis of Wilson’s model of information behavior [], this study divided the factors influencing the use of eHealth services into digital technology factors, health status, and general demographic characteristics.

Digital Technology Factors

eHealth literacy [], perceived usefulness, and perceived ease of use [] are categorized as digital technology factors.

The eHealth literacy scale (eHEALS), an 8-item instrument, was used to measure eHealth literacy []. Responses were collected using a 5-point Likert scale, ranging from very inconsistent to very consistent. The total score, calculated as the sum of scores across all 8 items, ranged from 8 to 40, with higher scores indicating greater eHealth literacy. eHEALS has demonstrated strong validity and reliability in China []. In this study, the Cronbach α for eHEALS was .976, indicating excellent internal consistency.

To measure inpatients’ acceptance of eHealth services, the constructs of perceived usefulness and perceived ease of use, derived from the Technology Acceptance Model proposed by Davis, were used []. Each construct was measured using 4 items, scored on a 5-point Likert scale, ranging from very inconsistent to very consistent. Total scores ranged from 4 to 20, with higher scores reflecting a more favorable evaluation of eHealth services. In this study, Cronbach α was .944 for perceived usefulness and 0.969 for perceived ease of use, indicating high reliability.

Health Status

Health status included self-rated health (SRH) and chronic disease (no=1, yes=2). SRH was initially assessed using 5 categories: very poor, poor, fair, good, and very good. For the purposes of this study, SRH was reclassified into 3 categories: negative (very poor or poor), fair, and positive (good or very good).

General Demographic Characteristics

General demographic characteristics also were selected in this study, such as sex (male=1, female=2), age (in years), marital status (married=1, unmarried=2), educational attainment (in years), and place of residence (urban=1, rural=2). Income, categorized into 3 groups based on per capita disposable household income and the average per capita urban and rural household income, was also considered []: lowest (below 40% of the per capita disposable household income of Jinan), middle (between 40% and 100% of the average per capita disposable income of Jinan), and highest (above average per capita disposable income of Jinan).

AWAG Matrix Analysis

This study used a matrix analysis based on the 3-stage consumer purchase decision model, which includes cognition, interest, and final decision []. As illustrated in , individuals initially form an understanding or awareness of innovative things under the influence of both external factors and personal characteristics. On the basis of this awareness and their own needs, they develop a willingness to engage with the service. Ultimately, driven by their personal circumstances and external triggers, this willingness is transformed into actual adoption behavior [,].

‎

To assess the digital divide in eHealth services, the study measured the percentage of participants who were aware of, wanted, and adopted eHealth services. In addition, the adoption gap rate of eHealth services was calculated to quantify the disparities in eHealth service adoption across the population [], using the following formula:

${\displaystyle {\displaystyle \mathrm{G}\mathrm{a}\mathrm{p}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}=\frac{min\left(\mathrm{P}\mathrm{r}\left(\mathrm{A}\left(\mathrm{X}\right)\right),Pr\left(\mathrm{W}\left(\mathrm{x}\right)\right)\right)-\mathrm{P}\mathrm{r}\left(\mathrm{U}\left(\mathrm{X}\right)\right)}{min\left(\mathrm{P}\mathrm{r}\left(\mathrm{A}\left(\mathrm{X}\right)\right),Pr\left(\mathrm{W}\left(\mathrm{x}\right)\right)\right)}\times 100\mathrm{\%}}}$

where $\mathrm{P}\mathrm{r}\left(\mathrm{A}\left(\mathrm{X}\right)\right)$ represents the awareness rate for eHealth service x;

$\mathrm{Pr}\left(\mathrm{W}\left(\mathrm{x}\right)\right)$ represents the want rate for eHealth service x; and

$\mathrm{P}\mathrm{r}\left(\mathrm{U}\right(\mathrm{X})$ represents the adoption rate for eHealth service x.

Adoption gap rates range from 0% to 100%. Among those who were already aware of or wanted eHealth services, the adoption gap rate represents the percentage of individuals who have never used eHealth services. When the adoption rate is equal to the minimum value of the awareness rate and the want rate, the adoption gap rate is 0, indicating that all the people who have awareness or want for eHealth services have used them. Conversely, if no one has used eHealth services, the adoption gap rate is 100%.

On the basis of technology adoption lifecycle (bell curve) and the AWAG matrix method by Liang [], this study grouped the innovators and the early majority stage and subdivided the 4 types of adopters into 3 adjusted adoption lifecycle accumulation rates of 15%, 50%, and 85%, including innovators or early adopters, early majority, late majority, and laggards []. The middle point of 50% divides both the awareness rates and the want rates into 2 levels. The AWAG matrix is thus divided into 4 primary categories: opened group, perception deficiency group, desire deficiency group, and closed group. According to Liang’s studies [], the opened group includes individuals who are receptive to innovation, demonstrating a strong interest in seeking new information and exploring innovative ideas. Conversely, individuals classified as the closed group show little interest in innovation and are resistant to adopting new ideas. The perception deficiency group includes individuals who lack a strong awareness of innovation. While they remain open to new information and are willing to explore innovative things, they tend to lag behind in receiving new information. The desire deficiency group, on the other hand, consists of individuals who, despite being early recipients of new information, are not interested in innovation and even show resistance to trying something new. Using the cumulative rate of 15% or 85%, each category was further divided into 4 subcategories, that is, strong (S), generic (G), want-bias (Wb), and awareness-bias (Ab). The strong subgroup represents the most open, closed, perception deficient, or desire deficient group, whereas the generic subgroup represents the least in each respective category. The position of each circle on the matrix reflects the awareness and want rates for eHealth services, whereas the size of the circle indicates the adoption gap rate. Larger circles correspond to greater usage gaps for a specific eHealth service, implying lower overall utilization. For instance, an eHealth service in the Wb opened group has high awareness but lagging desire, suggesting a need for strategies that build trust and perceived usefulness rather than mere information campaigns. Conversely, an eHealth service in the Ab opened group has strong desire but low awareness, indicating that marketing and education efforts should be prioritized.

Statistical Analysis

In the descriptive analysis, the mean and SD were used to summarize continuous variables with a normal distribution, whereas median and interquartile range (IQR) were adopted to describe those with a nonnormal distribution, including age, educational attainment, eHealth literacy, perceived usefulness, and perceived ease of use. For categorical variables, such as sex, marital status, place of residence, economic status, SRH, and chronic disease status, frequency and percentage were calculated to describe their distributions. Sample description and univariate analysis results are presented in Tables S1, S2, and S3 in the .

To identify significant factors influencing the awareness of, want for, and adoption of eHealth services, binary logistic regression analysis was conducted, adjusting for potential confounding factors. The results were reported as odds ratios with corresponding 95% CIs, and statistical significance was set at a P value <.05 (2 sided). To account for potential clustering effects within hospitals, cluster-robust SEs were used in the logistic regression analysis. All statistical analyses were performed using Stata 17.0 (StataCorp LLC, USA), and the AWAG matrix figure was generated using MATLAB R2019b (The MathWorks, Inc, USA).

Results

Descriptive Statistics

Nearly half of the participants were male (611/1322, 46.2%). The median age was 53 years (IQR 40‐60). Of 1322 participants, 174 (13.2%) were unmarried. Rural residents accounted for 21.6% (285/1322), whereas the majority resided in urban areas (1037/1322, 78.4%). The median years of educational attainment was 9 (IQR 6‐13). The largest income group was those with the lowest income, representing 52.8% (698/1322) of the sample.

SRH showed a balanced distribution between negative (326/1322, 24.7%) and positive (388/1322, 29.3%) assessments, with the largest proportion reporting fair SRH (608/1322, 46.0%). Most inpatients had chronic diseases, accounting for 60.67% (802/1322) of the sample. Among the 3 digital technology factors, a median eHealth literacy score of 26 (IQR 16‐32) was reported, whereas the median scores of perceived usefulness and perceived ease of use were 16 (IQR 13‐17) and 14 (IQR 9‐16), respectively. The detailed information regarding inpatients is displayed in Table S1, S2, and S3 in .

AWAG Matrix Analysis Results

presents the awareness, want, and adoption rates for eHealth services, including information-based, treatment intermediary, and treatment eHealth services. Overall, the awareness, want, and adoption rates for eHealth services were relatively high (all exceeding 50%), 1204 of 1322 inpatients (91.1%) had awareness of eHealth services, 88.4% (1169/1322) of them had a want for eHealth services, and 847 of 1322 inpatients (64.1%) adopted 1 or more of these services. The adoption gap ratio of eHealth services was 27.6%, categorizing it within the strong opened group. Among the 3 types of eHealth services, treatment intermediary eHealth services demonstrated the highest awareness (1182/1322, 89.4%), want (1142/1322, 86.4%), and adoption (753/1322, 57.0%) rates, with an adoption gap ratio of 34.1%. Conversely, treatment eHealth services showed the lowest rates, with awareness at 74.6% (986/1322), want at 69.9% (924/1322), and adoption at 23.5% (310/1322). The adoption gap ratio for treatment eHealth services was the highest, at 66.5%. As shown in , the adoption gap ratio for treatment eHealth services (66.5%) was the highest and fell into the generic opened group. In contrast, information-based eHealth services had the lowest adoption gap ratio (32.1%), placing it within the Wb opened group.

‎

Logistic Analysis of Awareness, Want, and Adoption on 3 eHealth Services

presented logistic analysis results of 3 different eHealth services. Regarding general demographic characteristics, older patients were less likely to have awareness of, want for, and adoption of all 3 eHealth services (P<.001), except for want for and adoption of treatment eHealth services (P>.05). Inpatients living in rural areas were less likely to have a want for all 3 eHealth services (P<.05). Educational attainment displayed a significant positive association with awareness and adoption of these 3 services (P<.01). Compared to inpatients with the lowest income, inpatients with middle income were more likely to have awareness of information-based and treatment intermediary eHealth services, whereas those with the highest were more likely to have a want for treatment intermediary and treatment eHealth services (P<.05).

Health status was an important factor that influenced awareness, want, and adoption in eHealth services among inpatients. Inpatients with more positive SRH were less likely to have awareness of, want for, and adoption of 3 services (P<.05). Having chronic diseases was only significantly negative with awareness of treatment intermediary and treatment eHealth services and adoption of treatment eHealth services (P<.01).

Among digital technology factors, eHealth literacy demonstrated a positive correlation with awareness of all 3 services (P<.001), and it had a favorable influence on want for information-based eHealth services and adoption of information-based and treatment eHealth services (P<.05). Perceived usefulness exerted a positive effect on both want for and adoption of 3 services (P<.001). Finally, perceived ease of use had a positive influence on the adoption of treatment intermediary and treatment eHealth services (P<.05).

Among inpatients, age, living in rural areas, and better SRH negatively influenced awareness, want, and adoption in eHealth services, but educational attainment, eHealth literacy, perceived usefulness, and perceived ease of use were positively associated with these outcomes. In addition, the influence of these factors differed depending on the specific type of eHealth service. The logistic analysis results for awareness of, want for, and adoption of eHealth services were presented in Table S4 in .

Discussion

Principal Findings

The findings of this study confirmed the existence of a digital divide in eHealth services among information-based, treatment intermediary, and treatment eHealth services. In addition, the study further demonstrated a reciprocal relationship between the want and awareness rate, suggesting that a higher awareness rate may stimulate greater want rate for eHealth services, and consequently, adoption rate of eHealth services also tends to increase with elevated awareness and want rates. These observations align with the findings of Te-Hsin Liang’s research on 2 types of eHealth services in Taiwan []. However, it is important to note that these findings are based on a sample from 3 hospitals in a single city in China, which may limit the generalizability of the results to other regions or populations.

In the AWAG matrix, information-based, treatment intermediary, and treatment eHealth services were all categorized within the opened group. Compared to other studies, inpatients in this research exhibited relatively high levels of awareness and want for these services. The internet, recognized as a rapidly expanding platform for health information dissemination [,], has emerged as a pivotal platform for accessing comprehensive health-related data, effectively catering to diverse health care stakeholders []. Inpatients, specifically, rely heavily on the internet to remain informed about their health status and to educate themselves on disease treatments []. While treatment intermediary eHealth services were positioned within the strong subgroup, information-based eHealth services were categorized in the Wb subgroup. Previous research highlights an increasing reliance on internet searches when addressing health concerns []. However, challenges persist regarding the quality of online health information []. Overuse or inappropriate use of information-based eHealth services can lead to exaggerated or misinterpreted adverse symptoms, potentially heightening health anxiety or fear [,]. Governmental support and hospital-led initiatives to raise awareness and promote the use of these services are instrumental in their effectiveness [,]. The adoption gap between treatment intermediary and information-based eHealth services was comparable, indicating similar potential for increased utilization of treatment intermediary eHealth services. This highlights the importance of developing a robust maintenance strategy. Several pivotal factors contributing to the digital divide in the adoption of such services have been identified through research, including inadequate staffing levels in hospitals, outdated medical technology, system implementation challenges, and the overarching health care environment []. However, thanks to policy support and advancements in medical technology [], the adoption gap rate for eHealth services in this study is notably lower compared to that reported in comparable research endeavors [].

Furthermore, treatment eHealth services fell within the generic subgroup characterized by relatively lower rates of awareness and want compared to the other 2 service categories. The late introduction of treatment eHealth services has led to limited awareness among inpatients []. Some studies have also found that this lack of awareness is further exacerbated by the higher eHealth literacy requirements for using treatment eHealth services [], which contributes to lower want rates []. Similar findings were observed in this study, showing that eHealth literacy positively influenced awareness of treatment eHealth services, while the want for these services remained relatively low. Notably, the adoption gap rate for treatment eHealth services was 66.5%, suggesting ample prospects for augmenting their adoption. Addressing barriers such as inadequate awareness and low eHealth literacy could play a pivotal role in narrowing this gap and enhancing the utilization of treatment eHealth services.

Factors influencing all 3 eHealth services consistently indicated that younger participants were more likely to be aware of and want eHealth services, whereas older participants demonstrated a greater propensity to adopt them. The decline in physical function commonly observed in older patients presents challenges in using electronic devices [], thereby perpetuating the digital divide. Furthermore, apart from the lack of access to digital devices [], the process of learning to use the internet may evoke feelings of anxiety or embarrassment among older adults []. Cao et al [] demonstrated that weekly online and offline knowledge and psychological interventions significantly improved the knowledge, willingness, confidence, and usage of internet medical services among older patients with chronic diseases in China. This suggests that the digital divide in eHealth services, driven by nonmaterial barriers among the older, can be mitigated through targeted knowledge training and mindset improvement, particularly in middle- and high-income countries [].

Rural participants were less likely to be aware of, want, and adopt eHealth services, consistent with previous findings and further substantiating the digital divide [,]. Likewise, negative SRH has a negative effect on eHealth services’ awareness, want, and adoption. This finding aligns with Andersen and Newman’s individual determinants of disease levels [], which suggest that it is the direct cause of want for and adoption of eHealth services among inpatients in this study. A study on telemedicine in the United States also found that residing in rural areas and access to broadband had a greater impact on the use of telemedicine than other socioeconomic factors, which highlights the importance of understanding not only broadband access but also the broader relationship between the rural environment and telemedicine use [].

The impact of income and educational attainment on eHealth services varied notably depending on the specific type of service. Compared to treatment intermediary eHealth services [,], information-based eHealth services typically have lower costs and are easier to use []. Similarly, this study also identified a significant positive effect of educational attainment on awareness and adoption of all 3 types of eHealth services. The research by Limbu and Huhmann [] and Reinecke et al [] further support these findings, highlighting that both income and education serve as important enablers for access to more expensive or complex eHealth services, regardless of whether in high-income or middle- and low-income countries, in which reforms are expected to address the digital divide. However, inpatients with poor SRH and chronic diseases were likely to want or adopt all 3 eHealth services, especially treatment services. Some studies have indicated that individuals with poor health conditions or chronic diseases exhibit a heightened demand for eHealth services that can address health issues and provide monitoring, potentially overcoming barriers to usage [,].

Moreover, higher eHealth literacy implies a stronger ability to acquire information and benefit from eHealth services []. Beyond its positive influence on awareness, eHealth literacy was also significantly associated with the want for information-based eHealth services and the adoption of treatment eHealth services. Specifically, younger patients with higher eHealth literacy scores demonstrated a greater likelihood of desiring and adopting these services, aligning with the findings of other studies conducted globally [-].

Perceived usefulness has a significant positive impact on eHealth services’ demand and use, consistent with previous studies [,]. However, in this study, perceived ease of use only has a positive impact on the adoption of eHealth services. This result aligns with prior research, suggesting that perceived ease of use indirectly influences the intention to use eHealth services through perceived usefulness, rather than exerting a direct effect []. In addition, a survey by Wu et al [] on inpatient participation in telemedicine in Toronto reveals that perceived usefulness, along with prior positive experiences, was a key factor driving participants’ willingness to engage in various telemedicine services. These results further underscore the importance of enhancing patients’ experiences and perceptions, as doing so can foster greater use of eHealth and help address the digital divide.

This study holds significant implications for eHealth policies and practices. The AWAG matrix analysis shows a digital divide in eHealth exists among inpatients in Jinan, considering various adoption stages and service types. Future digital health initiatives should avoid adopting a one-size-fits-all strategy and, instead, aim to achieve digital health equity through tailored services that account for varying user characteristics and needs. Information-based services were characterized by want bias. To enhance the credibility of information, it is recommended to introduce doctor certification and user evaluation systems, whereas targeted promotional efforts should be directed toward rural and low-education groups, using community health lectures, bulletin boards, and broadcasts in primary health care institutions. These low-cost measures can be followed by low- and middle-income countries. For treatment intermediary services with higher awareness, want, and adoption, it is essential to optimize operational processes, improve system stability, and enhance response speed to elevate the user experience. This can be achieved by simplifying appointment scheduling, payment procedures, and query results. However, the awareness, want, and adoption rate of medical treatment remain relatively low. It is crucial for the government and hospitals to collaborate to promote policy support. Therefore, collaboration between the government and health care institutions is critical to foster policy support. For example, governments should include eHealth or telemedicine services within the scope of medical insurance reimbursement and encourage both doctors and patients to use online follow-up services.

Limitations

This study acknowledges several limitations. First, it uses a cross-sectional survey design, which inherently restricts the ability to analyze influence pathways and establish causality among the relevant variables. Future studies adopting a longitudinal design would be more appropriate to address this limitation. Second, the sample population consists of inpatients from 3 hospitals in Jinan, Shandong Province, who inherently exhibit a certain level of medical service demand. Consequently, caution should be exercised when generalizing the findings to other populations. Finally, as a retrospective study, it is susceptible to recall bias, which could impact the accuracy of the data and results.

Conclusions

This study delves into inpatients’ awareness of, want for, and adoption of eHealth services by using a matrix analysis conducted among inpatients at 3 hospitals in Jinan, China. Information-based eHealth services, categorized within the opened Wb group in the AWAG matrix, revealed a significant digital divide in their usage. To address this, targeted strategies should focus on enhancing privacy protection and improving the perceived ease of use of these services, which could help sustain and gradually increase demand. Treatment intermediary eHealth services, classified within the opened strong group, also demonstrated a substantial digital divide in adoption, necessitating further attention. Finally, treatment eHealth services, positioned in the opened generic group, continue to face significant adoption challenges, with both awareness and want requiring improvement. To bridge these gaps, initiatives such as frequent public awareness campaigns and improving response efficiency need to be implemented to enhance awareness and foster sustained demand. The findings also underscore the diverse health care needs of individuals, shaped by factors such as educational attainment and place of residence. These differences necessitate comprehensive strategies, particularly in addressing the challenges older adults face in navigating internet technologies.

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Sarah Bennett/National Trust/PA Media

• Hyundai IONIQ 9 off-road concept codesigned by famed automotive personalities Jeremiah Burton and Zach Jobe, the founders of BigTime
• Lifted suspension, auxiliary lighting, and unique wheels paired with aggressive all-terrain tires enhance IONIQ 9’s adventure-ready appeal
• Set to be displayed in SEMA’s Future Tech Studio 
• IONIQ 9 Calligraphy AWD delivers exhilarating electric power to the road with up to 422 horsepower and 516 lb-ft of torque[i]

LAS VEGAS, Oct. 30, 2025 – Today, Hyundai revealed concept of its IONIQ 9 off-road SEMA concept, an all-electric SUV being developed with Jeremiah Burton and Zach Jobe, the renowned content creators at BigTime™. The concept will be displayed in SEMA’s Future Tech Studio; a dedicated section of the event’s Central Hall focused on EVs and innovative technologies from across the automotive industry. The IONIQ 9 BigTime concept features a lifted suspension, unique wheels and aggressive all-terrain tires. A set of custom auxiliary light bar further enhance the vehicle’s rugged, adventure-ready capability.

“Hyundai is more than proud to present the innovative IONIQ 9 off-road concept developed by BigTime at the 2025 SEMA show,” said Sean Gilpin, Chief Marketing Officer, Hyundai Motor North America. “Its aggressive lift, all-terrain tires and rugged off-road design inspire both innovation and customization — the hallmarks of any successful SEMA concept. This concept takes IONIQ 9 into new off-road terrain it has yet to explore, and we’re confident it’s more than up for the adventure.”

   
Hyundai’s IONIQ 9 SEMA concept is photographed in Los Angeles, Calif., on
Oct. 29, 2025

“We’re stoked to be working with Hyundai on the IONIQ 9. EVs have come a long way, so getting a chance to put our own spin on an off-road-themed IONIQ 9 is pretty cool,” said Jeremiah Burton, Chief Executive Officer at BigTime. “We believe all cars are cool, no matter what you drive. And bringing some elements from the off-road scene to the EV scene can hopefully inspire some future off-roaders.”

“We themed this IONIQ 9 off of our 1977 vintage cabover we call “Bud”. This new-tech-meets-vintage look makes the car stand out, and with some performance mods, make it a capable off-roader. It’s exciting to be part of this new chapter where electric cars can be fun and functional but still have character.”

   
Hyundai’s IONIQ 9 SEMA concept is photographed in Los Angeles, Calif., on
Oct. 29, 2025

BigTime
The BigTime content venture, led by automotive personalities Jeremiah Burton and Zach Jobe, are known for their creative capabilities in developing innovative concepts that amplify and expand the imagery of popular consumer vehicles. Jeremiah and Zach are well-known and respected figures at the SEMA show, having built a loyal audience, both online and in-person. Their deep knowledge of cars, combined with their accessible and entertaining content style, makes them ideal for telling stories of how vehicle modification can be simple, rewarding, and inclusive. The IONIQ 9 concept will receive extensive focus on BigTime’s various social platforms, including Instagram, with a number of build-stories expanding on the unique concept.

Hyundai Motor America
Hyundai Motor America offers U.S. consumers a technology-rich lineup of cars, SUVs, and electrified vehicles, while supporting Hyundai Motor Company’s Progress for Humanity vision. Hyundai has significant operations in the U.S., including its North American headquarters in California, the Hyundai Motor Manufacturing Alabama assembly plant, the all-new Hyundai Motor Group Metaplant America, and several cutting-edge R&D facilities. These operations, combined with those of Hyundai’s 850 independent dealers, contribute $20.1 billion annually and 190,000 jobs to the U.S. economy, according to a published economic impact report. For more information, visit www.hyundainews.com.

Hyundai Motor America on Twitter | YouTube | Facebook | Instagram | LinkedIn | TikTok

 

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[i] Standard on Limited, Calligraphy, and Calligraphy Design trims. Up to 422 hp (157.3 kW + 157.3 kW) standard with 700 Nm (516 lb.-ft.) of AWD torque. Actual horsepower will vary with options, driving conditions, driving habits and vehicle’s condition.

A massive online leak has exposed more than 183 million stolen email passwords gathered from years of malware infections, phishing campaigns and older data breaches. Cybersecurity experts say it is one of the largest compilations of stolen credentials ever discovered.

Security researcher Troy Hunt, who runs the website Have I Been Pwned, found the 3.5-terabyte dataset online. The credentials came from infostealer malware and credential stuffing lists. This malware secretly collects usernames, passwords and website logins from infected devices.

Researchers say the data contains both old and newly discovered credentials. Hunt confirmed that 91% of the data had appeared in previous breaches, but about 16.4 million email addresses were completely new to any known dataset.

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Discord Confirms Vendor Breach Exposed User Ids In Ransom Plot

The leak puts millions of users at risk. Hackers often collect stolen logins from multiple sources and combine them into large databases that circulate on dark web forums, Telegram channels and Discord servers.

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If you have reused passwords across multiple sites, attackers can use this data to break into your accounts through credential stuffing. This method tests stolen username and password pairs on many different platforms.

The risk remains real for anyone using old or repeated credentials. One compromised password can unlock social media, banking and cloud accounts.

Google Confirms Data Stolen In Breach By Known Hacker Group

Google confirmed there was no Gmail data breach. In a post on X, the company stated “reports of a Gmail security breach impacting millions of users are false. Gmail’s defenses are strong, and users remain protected.”

Google clarified that the leak came from infostealer databases that compile years of stolen credentials from across the web. These databases are often mistaken for new breaches when, in fact, they represent ongoing theft activity. Troy Hunt also confirmed the dataset originated from Synthient’s collection of infostealer logs, not from a single platform or recent attack. While no new breach occurred, experts warn that leaked credentials remain dangerous because cybercriminals reuse them for future attacks.

To see if your email was affected, visit Have I Been Pwned. It is the first and official source for this newly added dataset. Enter your email address to find out if your information appears in the Synthient leak.

Many password managers also include built-in breach scanners that use the same data sources. However, they may not yet include this new collection until their databases update.

If your address shows up, treat it as compromised. Change your passwords immediately and turn on stronger security features to protect your accounts.

Columbia University Data Breach Hits 870,000 People

Protecting your online life starts with consistent action. Each step below adds another layer of defense against hackers, malware and credential theft.

Start with your most important accounts, such as email and banking. Use strong, unique passwords with letters, numbers and symbols. Avoid predictable choices like names or birthdays.

Never reuse passwords. One stolen password can unlock multiple accounts. Each login should be unique to protect your data.

A password manager makes this simple. It stores complex passwords securely and helps you create new ones. Many managers also scan for breaches to see if your current passwords have been exposed.

Next, check whether your email has been caught in a recent credential leak. Our No. 1 password manager pick includes a built-in Breach Scanner that searches trusted databases, including the newly added Synthient data from Have I Been Pwned. It helps you find out if your email or passwords have appeared in any known leaks. If you see a match, change any reused passwords right away and secure those accounts with strong, unique credentials.

Check out the best expert-reviewed password managers of 2025 at Cyberguy.com.

Turn on 2Fa wherever possible. It adds a powerful second layer of defense that blocks intruders even if they have your password. You will receive a code by text, app or security key. That code ensures only you can log in to your accounts.

Identity Theft companies can monitor personal information like your Social Security number (SSN), phone number and email address, and alert you if it is being sold on the dark web or being used to open an account. They can also assist you in freezing your bank and credit card accounts to prevent further unauthorized use by criminals. It’s a smart way to stay one step ahead of hackers.

See my tips and best picks on how to protect yourself from identity theft at Cyberguy.com.

Infostealer malware hides inside fake downloads and phishing attachments. A strong antivirus software scans your devices to stop threats before they spread. Keep your antivirus updated and run frequent scans. Even one unprotected device can put your whole digital life at risk.

The best way to safeguard yourself from malicious links that install malware, potentially accessing your private information, is to have strong antivirus software installed on all your devices. This protection can also alert you to phishing emails and ransomware scams, keeping your personal information and digital assets safe.

Get my picks for the best 2025 antivirus protection winners for your Windows, Mac, Android and iOS devices at Cyberguy.com.

Browsers are convenient but risky. Infostealer malware often targets saved passwords in your web browser.

Updates fix security flaws that hackers exploit. Turn on automatic updates for your operating system, antivirus and apps. Staying current keeps threats out.

Avoid unknown websites that offer free downloads. Fake apps and files often contain hidden malware. Use official app stores or verified company websites.

Check your accounts regularly for unusual logins or device connections. Many platforms show a login history. If something looks off, change your password and enable 2FA immediately.

The massive leak of 183 million credentials shows just how far your personal information can spread and how easily it can resurface years later in aggregated hacker databases. Even if your passwords were part of an old breach, data like your name, email, phone number or address may still be available through data broker sites. Personal data removal services can help reduce your exposure by scrubbing this information from hundreds of these sites.

While no service can guarantee total removal, they drastically reduce your digital footprint, making it harder for scammers to cross-reference leaked credentials with public data to impersonate or target you. These services monitor and automatically remove your personal info over time, which gives me peace of mind in today’s threat landscape.

Check out my top picks for data removal services and get a free scan to find out if your personal information is already out on the web by visiting Cyberguy.com.

Get a free scan to find out if your personal information is already out on the web: Cyberguy.com.

This leak highlights the ongoing danger of malware and password reuse. Prevention remains the best defense. Use unique passwords, enable 2FA and stay alert to keep your data safe. Visit Have I Been Pwned today to check your email and take action. The faster you respond, the better you protect your identity.

Have you ever discovered your data in a breach? What did you do next? Let us know by writing to us at Cyberguy.com.

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Original article source: 183 million email passwords leaked: Check yours now

In the wake of the 2007-08 global financial crisis, green finance has been increasingly celebrated as a way to tackle environmental challenges. Banks, investment funds and insurers have rolled out a growing range of green products, from green bonds to sustainability-linked loans. This momentum is encouraged by international environmental efforts such as the Paris climate agreement.

By aligning financial flows with sustainability goals, the world can supposedly “green finance” its way into a sustainable future.

But beneath this green spectacle lies a more complicated reality. Green finance refers to a wide-ranging mix of private and public funds, products and practices. For example, there’s no consensus regarding what makes a bond green.

There is also little clarity around what current environmental, social, governance (ESG) frameworks – which encourage businesses and authorities to disclose and monitor their environmental and social performance – are truly achieving.

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Ever wondered how to spend or invest your money in ways that actually benefit people and planet? Or are you curious about the connection between insurance and the climate crisis?

Green Your Money is a new series from the business and environment teams at The Conversation exploring how to make money really matter. Practical and accessible insights from financial experts in the know.

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In 2015, the former Bank of England governor and current Canadian prime minister, Mark Carney, insisted that finance can and must urgently account for climate risks. Meanwhile, Stuart Kirk, former global head of responsible investments at high street bank HSBC, argued that these risks were overstated and too far in the future to be material.

Environmental issues have become a concern for financiers, but not necessarily out of commitment to improving planetary health – rather due to reporting costs, transition risks and reputational pressure. High-profile greenwashing scandals, such as “green bonds” allegedly linked to deforestation in Sumatra, have further eroded trust. This raises questions about whether green finance is more a branding exercise than transformation.

In the face of these ambiguities, the environmental sciences are involved in the expansion of green finance. As social scientists we have been following these developments, wondering whether they may help us pin down robust ways to develop green finance.

Some companies are now using science-based targets (emission reduction goals aligned with climate science), net zero transitions pathways or roadmaps, and high-integrity carbon credits (verified purchases of direct air capture credits to offset greenhouse gas emissions).

Most of these claim to rely on rigorous calculations. The language of science grants objectivity and legitimacy. At its most basic level, this “sciencewashing” uses the vocabulary and authority of science to claim sustainability outcomes.

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Read more:
Green bonds can help finance clean energy – as long as the projects they fund are transparent

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Green finance also provides many employment opportunities for environmental scientists who can work as consultants, auditors and certifiers, to assess the quality of green claims. Many startups have emerged, offering a range of high-tech services to provide environmental data to companies. That includes monitoring deforestation through remote sensing or using sounds to analyse wildlife activity.

Green finance-related industries are flourishing and more and more environmental graduates are being recruited to quantify emissions, build risk metrics, monitor changes in biodiversity and verify credits.

Sciencewashing

Drawing on five years of research and combining data emerging from participation in green finance conferences and seminars, interviews and document analysis, our study warns against different forms of sciencewashing.

Mounting evidence suggests a gap between the suggested possibilities and the actual outcomes of green finance. Many green finance products appear to serve financial markets and the wealthiest investors more than nature or vulnerable communities.

Even more concerning are the unintended consequences. Far from levelling the playing field, green finance can exacerbate inequality. For example, communities have been displaced to make room for renewable energy projects or offset schemes.

This creates what are known as green sacrifice zones: areas where environmental harm or social costs are tolerated in the name of advancing “green” goals.

Poorer countries often face higher borrowing costs in the name of climate risk, while wealthy economies continue to access cheaper capital. Insurance premiums are also rising in climate-vulnerable regions, pricing out those least able to afford them. So green finance can make the situation for the most vulnerable populations worse.

In its current form, green finance will most likely sustain business as usual, leaving the causes of environmental crisis untouched.

For green finance to deliver the transformative change its advocates promise, it must address the deeper political and social issues, such as the role of public authorities in regulating finance, or the relationship between green investment and global inequality.

If green finance is to serve collective wellbeing rather than the interests of a privileged few, we need rigorous and proactive public regulations and better public debates on what green finance ought to account for.

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Don’t have time to read about climate change as much as you’d like?

Get a weekly roundup in your inbox instead. Every Wednesday, The Conversation’s environment editor writes Imagine, a short email that goes a little deeper into just one climate issue. Join the 45,000+ readers who’ve subscribed so far.

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Unlock the Editor’s Digest for free

Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.

One of the top US officials overseeing OpenAI’s restructuring has warned she will take legal action against the ChatGPT maker if it fails to stick to public interest pledges Sam Altman agreed in negotiations to unlock the deal.

Kathy Jennings, the attorney-general of Delaware, told the Financial Times she consented to the deal after securing multiple binding legal commitments from Altman, OpenAI’s chief executive. They require the $500bn start-up to prioritise AI safety over its shareholders’ commercial gain.

The final agreement, announced on Tuesday, puts key decisions, including launching a public listing, in the hands of the non-profit OpenAI Foundation, which has a 26 per cent stake in the for-profit arm, called the OpenAI Group, worth $130bn.

“Anyone who is familiar with our work knows we are not shy to go into the courtroom to benefit the public if we need to,” said Jennings, who has previously taken legal action challenging Elon Musk’s so-called Department of Government Efficiency.

The complex restructure was finalised late on Monday night, after direct talks between Altman, Jennings and her California counterpart, Rob Bonta, according to multiple people with direct knowledge of the process.

OpenAI’s chief financial officer, Sarah Friar, worked in parallel to negotiate financial terms directly with Amy Hood, her counterpart at Microsoft, which is OpenAI’s biggest shareholder under the new structure, they added.

The deal, after more than a year of negotiations, enables investors to hold equity for the first time and unlocks a future stock market offering.

Altman said on Tuesday that an IPO was the most likely path for the AI group, though the company said it was too early to settle the timing or size of the raise. “An IPO is not our focus, so we could not possibly have set a date,” the company said.

As part of the restructuring discussions, Jennings and Bonta sought to codify language in OpenAI’s charter, which lays out principles to ensure AGI — AI which surpasses human intelligence — benefits humanity.

It also commits OpenAI to join forces with any “safety-conscious” rival that has a good chance of reaching OpenAI’s goal of creating AGI within a two-year timeframe.

“The charter was important to us [and was] one of the key concessions that we got,” said Owen Lefkon, a senior attorney in Jennings’s office, who worked on the agreement. “Up until this week, it was just a page on a website. As of today, the company has committed to two state attorneys-general that it will be used to execute the mission going forward.”

Jennings said Altman’s agreement to place OpenAI’s safety and security committee, which has the power to block the release of AI models, under the OpenAI Foundation rather than the OpenAI Group was also “a critical turning point in our discussions”.

“The question all along was whether OpenAI would reorient away from its charitable goals and towards profit-making. [Tuesday’s] agreement has meaningful provisions which mean the mission controls the operations,” said Jill Horwitz, a professor at Northwestern University and UCLA and expert in non-profit law.

These commitments mean OpenAI’s shareholders — including SoftBank and venture capitalists such as Thrive Capital and Khosla Ventures — are still exposed to unique risks from the start-up’s governance.

But it is unclear how some of these requirements will work in practice. Microsoft chief executive Satya Nadella on Tuesday questioned the definition of AGI, calling it a “nonsensical word”.

Microsoft, which has invested $13.75bn into OpenAI, agreed new terms with the start-up after Hood and Friar “took the deal offline and away from the lawyers” to iron out the final details last week, according to a person with knowledge of the matter.  

The companies signed a draft agreement on September 11, and set a 45-day countdown to finalise terms by this week. The last piece to fall into place was a pledge from OpenAI to spend $250bn with the technology giant’s cloud arm over time. 

“That was the final sticking point to be resolved and it got finalised over the weekend between Sarah and Amy,” said one person with knowledge of the talks. The pair both worked at Goldman Sachs in the early 2000s and have known each other for decades.

Microsoft, which will take a 27 per cent stake worth $135bn in the OpenAI Group, extracted another important concession: the group and its AI chief Mustafa Suleyman are now free to pursue AGI on its own and with third parties, having been prevented from doing so under the previous contract.

OpenAI, meanwhile, insisted on being able to ringfence significant pieces of “AGI-level” research from Microsoft, provided those were not commercialised, according to a person familiar with the discussions.

Pakistan’s foreign exchange reserves held by the State Bank of Pakistan (SBP) rose by $16 million over the past week, reaching $14.47 billion as of October 24, 2025, according to data released by the central bank on Thursday.

The SBP reported that the country’s total liquid foreign reserves now stand at $19.69 billion, with net reserves held by commercial banks recorded at $5.22 billion.

“During the week ended on 24-Oct-2025, SBP’s FX reserves increased by $16 million to $14,471.6 million,” the central bank said in its statement.

Despite the weekly decline, import cover remained stable at 2.36 months, reflecting continued support from central bank inflows and controlled external payments.

On a fiscal year-to-date basis, reserves are up by $419 million.

Compared to the start of the calendar year, reserves have improved by $3.76 billion, supported by stronger remittances and restrained import demand.

Market observers note that sustaining import cover above two months remains critical for exchange-rate stability ahead of scheduled IMF review milestones and external debt obligations.