Private credit, especially direct lending, has experienced rapid growth and transformation in recent years. As the asset class becomes a mainstream allocation, investors are shifting their attention to optimizing portfolio construction, risk management, and value creation. At the same time, rising interest rates and market volatility have further broadened the appeal of direct lending, attracting a diverse investor base ranging from individuals to large capital allocators and sophisticated issuers.
Amidst this growth and precarious backdrop, market participants will need to continue to refine their approach to underwriting and portfolio construction. Disciplined risk management and thoughtful diversification are essential for navigating today’s evolving landscape. Managers must maintain vigilance in underwriting, employing dynamic, bottom-up due diligence that evolves as markets shift. Specifically, managers need a strong understanding of complex geopolitical developments (such as tariff and trade policy) and new transformative technologies (such as Generative AI) to anticipate and avoid credit mistakes. In addition, a disciplined, top-down approach to portfolio construction remains just as crucial. Diversification across industries and issuers and limiting exposure to sectors prone to cycles or regulatory risk helps reduce overall portfolio risk.
An often-overlooked consideration is the importance of structure in direct lending transactions. Given current market dynamics, maintaining a risk-first mindset is essential, and thoughtful structuring can be a powerful driver of value in direct lending. Features such as faster capital calls, more efficient deployment, and potentially lower fees can enhance net returns while reinforcing the robust risk management highlighted above.
Investor Challenges with Traditional Drawdown Structures
Historically, direct lending was only offered via traditional drawdown structures. However, the rise of business development companies and other liquid vehicles in recent years has provided credit investors with additional options. While each of these structures has pros and cons, we believe a hybrid solution, the Institutional Open-Ended Structure, can provide investors with the best of both worlds.
Key Considerations for Investors When Evaluating Direct Lending Structures:
Familiarity:
For investors accustomed to traditional drawdown structures, vehicles with defined investment and harvest periods offer a familiar experience.
Simplicity:
A well-designed structure can provide pure-play direct lending exposure. This approach avoids accelerated liquidity options (reducing cash drag), gates, and the need to hold liquid, non-direct lending investments.
Optionality:
Investors should be able to retain control over key decisions, including: (i) whether to receive distributions or recycle income, and (ii) whether to remain invested or begin harvesting exposures. Each investor’s choices should operate independently without affecting others.
Addressing the Challenges Investors Face with Traditional Closed-End Structures
Challenge 1: Slower deployment and the J-Curve
In traditional drawdown structures, capital is called in a pro-rata fashion, whether it was committed months ago or the day before the final close. As a result, it can often take an investor 3-4 years to reach specific deployment targets. While this has been the industry standard for locked-up capital, many investors today are looking for faster ways to put capital to work to generate income and manage asset allocation targets.
Potential Solution:
We believe it is preferable to use a structure where capital is called from next quarter’s tranche only after the previous quarter’s tranche is fully called. By calling capital in this fashion, investors who committed earlier get their capital called first and deployed faster. Importantly, new investors enter the existing portfolio at the latest quarterly NAV and avoid blind pool risk by underwriting an existing portfolio during their diligence process.
EXHIBIT 1: Visual Representation of Tranche and Queue System
Apple is set to report its first quarterly earnings since the release of its new lineup of iPhones on Thursday. After the company hit a $4tn market value this week for the first time, analysts are expecting it to demonstrate steady financial growth and a strong bottom line despite slow progress on artificial intelligence.
The slate of new iPhones, in particular the iPhone 17 and 17 Pro, have reinvigorated demand for Apple’s products, especially in China, where sales had been lagging behind projections. Demand for the ultra-thin iPhone Air remains the subject of speculation, with some analysts saying that the company has lowered production of the device and others asserting it has not.
Wall Street is anticipating Apple to report $102bn in revenue and earnings of $2.53 per share for the fourth quarter of 2025, according to analyst group LSEG.
John Belton, a portfolio manager at Gabelli Funds, said the positive estimates are due to increasing iPhone sales along with a price increase with the device’s newest model. “The most bullish data point coming out of Apple’s last earnings report was the iPhone revenue number,” Belton said. “The double-digit growth represented the best iPhone growth in at least three years.”
The strong iPhone revenue comes even as Apple has lagged behind other tech companies in releasing artificial intelligence products. The company has yet to fully roll out an AI product to compete with companies like Meta, Google and Microsoft. Apple has also struggled with the up-and-down tariffs that Donald Trump has levied on China and India, where the vast majority of the company’s manufacturing takes place.
Nevertheless, Apple’s stock has risen over the past few weeks, inflating the company’s market cap, one of only three companies worldwide worth more than $4tn. Both Nvidia and Microsoft have also hit that milestone.
Apple’s share price has increased by more than 50% since a low point in April, which analysts credit to the debut of its latest products. Along with the iPhone 17, the company also launched new AirPod earbuds with live translation tools and upgrades to its Apple Watch lineup.
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Apple is reporting earnings this week, along with other top tech behemoths, including Microsoft, Meta, Amazon and Alphabet, as the wider US stock market hits record highs. Microsoft and Alphabet posted strong results Wednesday, while Meta’s were more mixed, leading to a slump in share price.
NEW YORK (Reuters) – Oil prices held steady on Thursday as investors assessed a potential trade truce between the United States and China after U.S. President Donald Trump lowered tariffs on China following a meeting with Chinese leader Xi Jinping in South Korea. Brent futures rose 8 cents, or 0.1%, to settle at $65.00 a barrel, while U.S. West Texas Intermediate (WTI) crude rose 9 cents, or 0.1%, to settle at $60.57. Trump agreed to reduce tariffs on China to 47% from 57% in a one-year deal in exchange for Beijing resuming U.S. soybean purchases, keeping rare earths exports flowing and cracking down on the illicit fentanyl trade. PVM analyst Tamas Varga said investors see the announced agreement between China and the U.S. as more of a de-escalation of tension than a structural change in the relationship.
Oil majors Shell and TotalEnergies posted quarterly profit falls of 10% and 2% respectively on Thursday, dragged down by lower oil prices, though Shell beat expectations, helped by better trading results in its huge gas division. FED RATE CUT LIFTS ECONOMIC OUTLOOK Also helping to boost the economic outlook, the U.S. Federal Reserve lowered interest rates on Wednesday, in line with market expectations. However, it signalled that it might be the last cut of the year as the ongoing government shutdown threatens data availability. Lower interest rates reduce consumer borrowing costs and could boost economic growth and oil demand. “The Fed’s decision underscores a broader turn in its policy cycle – one that favours gradual reflation and support over restraint, providing a tailwind to commodities sensitive to economic activity,” Rystad Energy’s chief economist Claudio Galimberti said in a note. In Europe and Asia, meanwhile, the European Central Bank and the Bank of Japan kept interest rates unchanged. The euro zone economy grew a touch more quickly than expected in the third quarter, lifted by buoyant growth in France and Spain that more than offset faltering exports and persistent struggles in Germany’s oversized industrial sector. In Germany, however, gross domestic product stagnated in the third quarter, data showed on Thursday, highlighting the struggle Europe’s biggest economy faces in regaining momentum as exports dwindle. OVERSUPPLY CONCERNS Both crude benchmarks were on track to decline by around 3% in October, which would be their third consecutive month of losses following concerns about oversupply. In the U.S., crude output hit a weekly record high of around 13.6 million barrels per day (bpd) last week. Investors said they were looking ahead to an OPEC+ meeting scheduled for November 2, where the alliance will likely announce another 137,000 bpd supply hike for December. OPEC+ includes the Organization of the Petroleum Exporting Countries (OPEC) and allies like Russia. In a series of monthly increases, eight OPEC+ members had boosted output targets by a total of more than 2.7 million bpd – or about 2.5% of global supply. In Saudi Arabia, the world’s top oil exporter, the budget deficit widened to 88.5 billion riyals ($23.60 billion) in the third quarter, a 160% rise from the previous quarter as spending increased and revenues fell, the finance ministry said on Thursday.
($1 = 3.7504 riyals)
(Reporting by Scott DiSavino in New York, Enes Tunagur in London, Mohi Narayan in New Delhi and Colleen Howe in Beijing; Editing by Edwina Gibbs, Jamie Freed, Bernadette Baum, Jane Merriman, Joe Bavier and Diane Craft)
North American Retail Growth: 9% increase, led by strong Off-Road performance.
Dealer Inventory: Down 21% year-over-year.
Adjusted EBITDA Margin: Under pressure due to increased tariffs and normalized incentive compensation.
Adjusted EPS: $0.41, driven by strong mix and operational efficiencies.
Operating Cash Flow: $159 million for the quarter.
Free Cash Flow: Approximately $485 million year-to-date.
Off-Road Sales Growth: 8%, supported by a richer mix of vehicles.
Marine Sales Growth: 20%, driven by positive shipments of new boats.
Gross Profit Margin: Impacted by $35 million in new tariffs.
Full Year Sales Guidance: $6.9 billion to $7.1 billion.
Full Year Adjusted EPS Guidance: Expected to be a loss of approximately $0.05.
Release Date: October 28, 2025
For the complete transcript of the earnings call, please refer to the full earnings call transcript.
Polaris Inc (NYSE:PII) reported strong third quarter results with sales reaching $1.8 billion, driven by improved retail and a solid mix of Off-Road vehicles.
The company gained approximately 3 points of market share in the Off-Road Vehicle (ORV) segment, led by strong performance in the Polaris RANGER and crossover vehicles.
Dealer inventory levels have improved significantly, with a 21% year-over-year reduction, leading to healthier inventory and lower flooring expenses for dealers.
Polaris Inc (NYSE:PII) successfully executed operational efficiencies, exceeding their goal of $40 million in structural operational efficiencies for the year.
The sale of a majority stake in Indian Motorcycle is expected to be accretive to adjusted EBITDA by approximately $50 million and to adjusted EPS by approximately $1, allowing Polaris to focus on high-margin growth opportunities.
Adjusted EBITDA margin was under pressure due to increased tariffs and normalized incentive compensation.
The company faced headwinds in the Youth segment due to a shift in production out of China, which is expected to continue into early Q4.
Tariffs are expected to have a significant impact, with an anticipated $90 million hit in 2025, increasing to over $200 million in 2026.
The On-Road segment experienced a decline in sales, driven by softness in the broader motorcycle market and within the Slingshot business.
Fourth quarter adjusted EPS is expected to be lower than the third quarter due to increased tariffs, negative mix, and higher operating expenses.
Q: What drove the 9% growth in ORV retail and share gains in the quarter? A: Michael Speetzen, CEO, explained that the growth was due to right-sized inventory, a broad RANGER lineup, and significant quality improvements. The RANGER 500 attracted new customers, and the NorthStar rewards program enhanced dealer performance, contributing to the share gains.
Q: What are the expectations for fiscal 2026, considering the Indian Motorcycle deal and tariffs? A: Michael Speetzen, CEO, noted that the Indian Motorcycle deal will significantly impact revenue but add $50 million in EBITDA and $1 in EPS. Tariffs are expected to be just over $200 million, but mitigation efforts are underway. The company anticipates a flat industry with potential growth from shipping aligning with retail demand.
Q: Can you provide insights into the consumer profile for the RANGER 500? A: Michael Speetzen, CEO, stated that the RANGER 500 attracts new customers who previously couldn’t find an entry point into Polaris products. These customers often have small properties and are new to powersports, transitioning from alternatives like golf carts.
Q: How did the Factory Authorized Clearance (FAC) program impact demand, and what are the expectations for Q4? A: Michael Speetzen, CEO, mentioned that the FAC program successfully generated excitement without significant cost increases. It helped reduce noncurrent inventory, and October trends indicate continued strength in key areas like RANGER XD and XPEDITION, with Q4 retail expected to rise in low single digits.
Q: What are the key takeaways from 2025, excluding tariffs, and what lessons have been learned? A: Robert Mack, CFO, highlighted that promotions were heavier than expected, but mix and plant performance exceeded expectations. Operational execution improved significantly, with plants outperforming targets, indicating strong future potential for operational improvements.
For the complete transcript of the earnings call, please refer to the full earnings call transcript.
Jerome Powell, chairman of the US Federal Reserve, during a news conference following a Federal Open Market Committee (FOMC) meeting in Washington, DC, US, on Wednesday, Oct. 29, 2025.
Al Drago | Bloomberg | Getty Images
Federal Reserve Chair Jerome Powell faces if not the most difficult challenge of his time in office at least the trickiest in his final months as head of the all-powerful U.S. central bank.
Fresh off his surprisingly tough talk Wednesday on the potential for another interest rate cut in December, Powell will have to steer his way through a suddenly contentious atmosphere among policymakers that will make whichever direction the Fed chooses divisive.
While it’s not the existential economic threat posed by the Covid pandemic in 2020, it nevertheless indicates a level of peril uncommon for the institution.
“December could get messy,” Bank of America economist Aditya Bhave said in a client note. “We still think the Fed won’t cut rates again under Chair Powell. But barring a clear signal in either direction from the data, the December decision will likely be even more contentious than October.”
The Fed on Wednesday approved a widely anticipated quarter percentage point rate reduction that took its benchmark rate down to 3.75%-4%. However, Powell warned that another cut in December “is not a foregone conclusion,” something the market was not expecting.
While Wall Street economists and strategists were split over whether the committee will in fact approve another reduction at the Dec. 9-10 meeting, they were in agreement that this is a pivotal moment for Powell and the legacy he ultimately will leave when his term runs out in May.
“Even in a situation without much additional data due to the shutdown, it can actually make sense to push against market pricing to keep optionality going forward,” wrote Michael Gapen, chief U.S. economist at Morgan Stanley. “A 95% probability assigned to a December cut does not seem consistent with a data-dependent Fed.”
Markets react
For their part, traders weren’t buying the hawkish rhetoric. Fed fund futures pricing Thursday still indicated a 75% probability of a cut in December, though that was down from around 90% the day before, according to the CME Group’s FedWatch.
But Powell went to great lengths in his post-meeting news conference Wednesday to dispel the notion that the reduction, which would be the third since September, is a slam dunk.
The thrust of his argument was multi-pronged: What data there is available during the government shutdown blackout has largely showed a stable economy though the labor market is a risk; inflation is still above target; and, in an unusual development, there are “strongly differing” views on the FOMC for where policy should move.
Markets were clearly caught off guard by the move, with stocks slipping and Treasury yields surging. The 10-year Treasury yield was solidly above 4% Thursday while the policy-sensitive 2-year note climbed over 3.6% to its highest level in about a month.
“The reaction of the bond market should certainly give Fed officials pause,” wrote Ed Yardeni, head of Yardeni Research and coiner of the term “bond vigilantes” to describe buyers’ strikes in the fixed income markets. “The bond market isn’t buying the Fed’s cover story that interest rates were too restrictive.”
For Powell, the statement regarding December was an unusual step considering markets had been expecting a more neutral tone. Asked whether he was bothered by the strong anticipation of another cut, Powell said markets should take his statement that a reduction “is not a foregone conclusion” should be “taken on board.”
“You’ve got get right in front of that, because you don’t want to surprise the market a couple weeks down the road. Now is the time to do it,” said Dan North, senior economist for North America at Allianz Trade. “He doesn’t usually use words quite so forcefully. So that was interesting, and he’s clearly trying to squash speculation about December. We feel the same way, December is going to be a pause.”
Political overhang
The developments come at a ticklish time for the Fed.
Powell, a favorite target for President Donald Trump’s criticism, has only seven months or so left in his term. Treasury Secretary Scott Bessent has been busy interviewing potential successors — among them current Governors Christopher Waller and Michelle Bowman, both of whom voted in favor of the cut.
In addition, Governor Stephen Miran, a hand-picked Trump appointee who will only serve through January, again dissented from the vote in favor of a half-point.
At the other end of the spectrum, Kansas City Fed President Jeffrey Schmid voted “no” as well, but because he wanted to not cut. Between them run a range of views on the normally consensus-driven FOMC.
Whether Powell’s tip of the hat to the doves reflects merely a courtesy or deeper misgivings about cuts will be central to Fed analysis in the coming weeks.
“While the press conference played out somewhat differently than we expected, we have not changed our Fed forecast and continue to see a December cut as quite likely,” Goldman Sachs economist David Mericle wrote. “We suspect that there is substantial opposition on the FOMC to the risk management cuts and that Powell thought it was important to voice other participants’ concerns today in his press conference. But we still think that the arguments for a December cut remain intact.”
OpenAI is reportedly gearing up for a stock market listing valuing the company at $1tn (£76bn) as soon as next year, in what would be one of the biggest ever initial public offerings.
The developer behind the hit AI chatbot ChatGPT is considering whether to file for an IPO as soon as the second half of 2026, according to Reuters, which cited people familiar with the matter. The company is thought to be looking to raise at least $60bn.
A stock market float would give OpenAI another route to raising cash, supporting ambitions by the chief executive, Sam Altman, to splash trillions of dollars on building datacentres and other forms of infrastructure needed for the rapid buildout of its chatbots.
During a staff livestream on Tuesday, Altman was reported to have said: “I think it’s fair to say it [an IPO] is the most likely path for us, given the capital needs that we’ll have.”
An OpenAI spokesperson said: “An IPO is not our focus, so we could not possibly have set a date. We are building a durable business and advancing our mission so everyone benefits from AGI.”
AGI stands for artificial general intelligence, which OpenAI defines as “highly autonomous systems that outperform humans at most economically valuable work”.
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OpenAI was founded as a nonprofit in 2015 with a mission to safely build AGI for humanity’s benefit. However, it completed a lengthy restructuring process this week that turned its main business into a for-profit corporation. While it is still technically controlled by the non-profit entity, the move makes it easier for OpenAI to raise capital while also laying the groundwork for an IPO.
The deal also gave Microsoft a stake of about 27% in the for-profit company, with OpenAI valued at $500bn under the terms of the deal. News of the restructuring helped push Microsoft’s valuation above $4tn for the first time.
OpenAI reportedly posted revenue of $4.3bn in the first half of this year, with an operating loss of $7.8bn, according to the tech news site the Information.
The massive valuations will do nothing to allay fears that the AI industry is operating in a bubble. Officials at the Bank of England this month flagged the growing risk that tech stock prices pumped up by the AI boom could burst, saying equity markets were “particularly exposed should expectations around the impact of AI become less optimistic”.
OpenAI’s chief financial officer, Sarah Friar, reportedly told colleagues that the company was aiming for a 2027 listing, according to the sources cited by Reuters, while some advisers said it could come the year before.
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 .
Table 1. Characteristics of 3 tertiary hospitals in Jinan for the eHealth survey from June to October 2023, including their eHealth service models and functional features.
Hospitals
Characteristic
A
Innovative “internet plus smart medical” model
One-stop-shop online follow-up service
Health science communication popularization matrix based on short video
B
Docking with Jinan “internet+medical health” convenience service platform
Digital inpatient ward system
The smart hospital built is in the forefront of Jinan municipal hospitals
C
The most functional internet hospital in the province
A full range of eHealth services
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.
where = 2.393, = 0.5, and = 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.
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 [-].
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 [-].
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 [-].
Table 2. Classification of 12 eHealth services by functional role as information-based, treatment intermediary, and treatment services.
Items
eHealth services
Information-based services
Seek disease or health information online
Seek doctor or hospital information online
Seek medical review information online (patients’ evaluation of doctors)
Give or receive peer-to-peer feedback about health status in online communities or chat platforms
Treatment intermediary services
Outpatient appointment booking online
Pay medical bills online
Access electronic medical records and medicinal examination reports online
Appoint a medical examination or surgery online
Online hospitalization appointment
Online drugstore purchases or online pharmacy, excluding dietary supplements
Treatment services
Electronic consulting, including email, chat, or video health care consultations
Chronic disease monitoring and management, online telemonitoring, or remote monitoring to manage chronic diseases, such as social networking or eHealth communities, telehealth, and mobile health, including wearable devices or apps
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 [,].
Figure 1. Schematic diagram of the association between awareness, want, and adoption.
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:
where represents the awareness rate for eHealth service x;
represents the want rate for eHealth service x; and
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.
Table 3. Awareness, want, and adoption gap of information-based, treatment intermediary, and treatment eHealth services.
Variables
Awareness, n (%)
Want, n (%)
Adoption, n (%)
Adoption gap ratio (%)
Region in awareness-want segment matrix
eHealth services
1204 (91.1)
1169 (88.4)
847 (64.1)
27.6
O _ S
Information-based eHealth services
1128 (85.3)
1037 (78.4)
704 (53.3)
32.1
O _ Wb
Treatment intermediary eHealth services
1182 (89.4)
1142 (86.4)
753 (57.0)
34.1
O _ S
Treatment eHealth services
986 (74.6)
924 (69.9)
310 (23.1)
66.5
O _ G
aGroups are opened (O), desire deficiency (D), perception deficiency (P), and closed (C); regions are strong (S), generic (G), awareness-bias (Ab), and want-bias (Wb).
Figure 2. Awareness, want, and adoption gap matrix for information-based, treatment intermediary, and treatment eHealth services.
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).
Table 4. Logistic regression results for awareness of, want for, and adoption of information-based, treatment intermediary, and treatment eHealth services.
Variables
Information-based eHealth service, OR (95% CI)
Treatment intermediary eHealth services, OR (95% CI)
Treatment eHealth services, OR (95% CI)
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Model 9
Awareness
Want
Adoption
Awareness
Want
Adoption
Awareness
Want
Adoption
Gender
Female (ref: male)
1.392 (0.720-2.689)
1.140 (0.857- 1.517)
0.895 (0.677- 1.183)
1.142 (0.631-2.068)
0.896 (0.537- 1.495)
1.003 (0.755- 1.331)
1.310 (1.097- 1.564)
0.863 (0.651- 1.142)
1.084 (0.937- 1.253)
Age
0.927 (0.904-0.951)
0.956 (0.947- 0.965)
0.956 (0.934- 0.978)
0.940 (0.911-0.970)
0.940 (0.926- 0.954)
0.950 (0.932- 0.969)
0.943 (0.922- 0.964)
0.989 (0.961- 1.018)
0.994 (0.987- 1.001)
Marital status
Single (ref: married)
0.598 (0.317-1.129)
0.896 (0.588- 1.367)
0.675 (0.544- 0.838)
0.573 (0.267-1.228)
0.784 (0.548- 1.120)
1.078 (0.739- 1.573)
0.671 (0.603- 0.747)
1.101 (0.785- 1.544)
1.502 (0.525- 4.297)
Place of residence
Rural (ref: urban)
0.447 (0.299-0.669)
0.524 (0.363- 0.756)
0.559 (0.308- 1.013)
0.604 (0.312-1.171)
0.350 (0.155- 0.793)
0.428 (0.237- 0.772)
0.605 (0.227- 1.613)
0.583 (0.554- 0.613)
0.797 (0.440- 1.444)
Educational attainment
1.104 (1.066-1.142)
1.022 (0.969- 1.077)
1.034 (1.018- 1.050)
1.118 (1.105-1.131)
1.068 (0.992- 1.149)
1.106 (1.073- 1.140)
1.089 (1.028- 1.153)
0.993 (0.979- 1.007)
1.031 (1.001- 1.063)
Income
Middle (ref: lowest)
1.424 (1.277-1.588)
1.599 (0.568- 4.507)
1.304 (0.679- 2.504)
1.205 (1.029-1.412)
0.901 (0.514- 1.578)
1.057 (0.810- 1.380)
0.726 (0.296- 1.785)
1.670 (0.884- 3.153)
0.919 (0.592- 1.427)
Highest (ref: lowest)
1.426 (0.805-2.527)
2.237 (1.758- 2.847)
1.231 (0.841- 1.800)
2.309 (0.938-5.683)
2.206 (0.998- 4.877)
1.471 (0.919- 2.355)
0.880 (0.266- 2.912)
1.399 (1.185- 1.651)
0.954 (0.733- 1.243)
SRH
Fair (ref: negative)
0.595 (0.475-0.746)
0.355 (0.189- 0.667)
0.597 (0.373- 0.953)
0.480 (0.417-0.552)
0.471 (0.386- 0.576)
0.581 (0.418- 0.806)
0.796 (0.543- 1.167)
0.630 (0.502- 0.791)
0.689 (0.594- 0.799)
Positive (ref: negative)
0.360 (0.328-0.396)
0.287 (0.248- 0.332)
0.319 (0.152- 0.671)
0.450 (0.177-1.143)
0.491 (0.318- 0.757)
0.485 (0.421- 0.558)
0.640 (0.343- 1.195)
0.523 (0.273- 1.001)
0.510 (0.430- 0.604)
Chronic disease
Yes (ref：no)
0.724 (0.427-1.228)
1.265 (0.529- 3.024)
1.275 (0.600- 2.712)
0.693 (0.509-0.944)
1.003 (0.488- 2.061)
0.789 (0.473- 1.315)
0.550 (0.328- 0.921)
0.829 (0.472- 1.457)
0.832 (0.742- 0.934)
eHealth literacy
1.133 (1.097-1.171)
1.050 (1.041- 1.058)
1.048 (1.016- 1.082)
1.082 (1.059-1.106)
0.963 (0.916- 1.013)
0.992 (0.960- 1.025)
1.114 (1.091- 1.138)
1.018 (0.979- 1.060)
1.062 (1.006- 1.120)
Perceived usefulness
—
1.365 (1.220- 1.528)
1.202 (1.180- 1.225)
—
1.379 (1.287- 1.479)
1.154 (1.109- 1.202)
—
1.335 (1.258- 1.417)
1.107 (1.088- 1.126)
Perceived ease of use
—
1.023 (0.854- 1.226)
1.046 (0.966- 1.132)
—
1.108 (0.938- 1.310)
1.110 (1.021- 1.207)
—
1.069 (0.929- 1.231)
1.095 (1.005- 1.193)
aOR: odds ratio.
bModel 1 (awareness), Model 4 (awareness), Model 7 (awareness): logistic regression model adjusted for sex, age, marital status, place of residence, educational attainment, income, SRH, chronic disease, and eHealth literacy.
cModel 2 (want) adds perceived usefulness and ease of use to the variables in model 1; model 5 (want) adds perceived usefulness and ease of use to the variables in model 2; model 8 (want) adds perceived usefulness and ease of use to the variables in model 7.
dModel 3 (adoption) includes all variables from model 2; model 6 (adoption) includes all variables from model 5; model 9 (adoption) includes all variables from model 8.
eP<.01.
fP<.001.
gP<.05
hSRH: self-rated health.
iNot applicable.
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.
This study was supported by the Humanities and Social Science Foundation of the Ministry of Education of China (grant 21YJC630060), the National Natural Science Foundation of China (grant 72274108), and the Natural Science Foundation of Shandong Province (grant ZR2022MG003). The funders had no involvement in the study design, data collection, analysis, interpretation, or the writing of the manuscript. The authors would like to thank the Shandong University School of Public Health and all participants for making this study possible.
Data will be made available on request.
WS and DD contributed to data curation, formal analysis, interpreted the data, and wrote the original draft of the manuscript. SD and ZY contributed to reviewing and editing the manuscript. JL acquired funding, administered the project, provided supervision, interpreted the data, and contributed to reviewing and editing the manuscript. All authors reviewed and approved the final paper before submission.
None declared.
Edited by Alicia Stone, Amaryllis Mavragani; submitted 07.Feb.2025; peer-reviewed by Sonia Butler, Xueting Ding; final revised version received 04.Oct.2025; accepted 06.Oct.2025; published 30.Oct.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
The tree was a favourite subject of photographers and artists
The stump of the beloved Sycamore Gap tree could be damaged by tributes left by visitors, the National Trust said.
The charity, which along with the Northumberland National Park Authority looks after the site by Hadrian’s Wall, has added a protective cage of wire mesh netting to the fenced-off stump to protect it.
It has shown signs it could regrow after it was maliciously cut down by two men from Cumbria, but visitors leaving physical tributes could put the tree at risk, the charity said.
Andrew Poad, the site’s general manager for the National Trust, said: “This regrowth is extremely fragile. Every step on the soil or contact with the stump risks damaging the tree’s chance of recovery.”
The tree was deliberately felled by Daniel Graham and Adam Carruthers, who travelled from Cumbria one night in September 2023 to chop it down.
They were convicted of criminal damage in July and sentenced to more than four years in prison.
The tree was a much-loved landmark that inspired photographers and artists.
Since its felling, some visitors have been leaving tributes such as stones or small items at the site by Hadrian’s Wall.
However, disturbing the remains of the tree or the ground around it could hamper any possible regrowth.
Sarah Bennett/National Trust/PA Media
Additional netting has been added to protect the stump
People have been asked to enjoy the site from the designated path and share their memories through “photographs and stories” rather than physical tributes.
“The regeneration of the stump offers hope to many people, and it has been uplifting to see the tree defiantly growing despite the trauma it endured,” said Tony Gates, CEO of the Northumberland National Park Authority.
“This is a moment for patience and care, allowing nature to do what it does best.”
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
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Hyundai’s IONIQ 9 SEMA concept is photographed in Los Angeles, Calif., on Oct. 29, 2025
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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.
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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.
