Category: 3. Business

Brett Keller to step down after 11 years of service

NEW YORK, Feb. 2, 2026 /PRNewswire/ — Broadridge Financial Solutions, Inc. (NYSE: BR), a global Fintech leader, is pleased to announce the appointment of Trish Mosconi and Christopher Perry as members of its Board of Directors, effective February 2, 2026. Following their appointment, Broadridge’s expanded Board will consist of 10 members, eight of whom are independent. Ms. Mosconi will serve on the Audit and Compensation Committees of the Board.

Broadridge also announced that Brett Keller, director since 2015 and member of the Audit and Compensation Committees, has notified the Company of his decision to resign from the Board, effective April 30, 2026. Mr. Keller advised the Board that his decision was based on an assignment to fulfill a full-time missionary leadership assignment with his wife, Marcie, in Japan.

“It has been a privilege to serve on the Broadridge Board during a period in which the Company has continued to strengthen its position as a global technology leader and a trusted and transformative partner to its clients,” said Brett Keller, Director of the Broadridge Board. “I look forward to all that the Company will accomplish in the years ahead.”

“On behalf of the entire Board and management, I want to sincerely thank Brett for his many years of dedicated service to Broadridge and our shareholders,” said Tim Gokey, Chief Executive Officer and Director of Broadridge. “It has been an honor to work alongside him, and we are grateful for his invaluable contributions. We wish him all the best as he pursues this meaningful next chapter.”

“I want to echo Tim’s sentiments in thanking Brett for his many contributions to Broadridge,” said Eileen Murray, Chairperson of Broadridge’s Board of Directors. “I am thrilled to welcome Trish and Chris, who are accomplished executives with deep experience in financial services. As the financial services industry continues to transform, their expertise will help ensure that Broadridge remains at the forefront of innovation as we continue to provide the infrastructure and technologies to support our clients’ growth and ultimately, enable better financial lives.”

Ms. Mosconi is a Senior Advisor to chief executive officers and boards of directors in the financial institutions, payments, fintech, digital transformation, and artificial intelligence industries at Boston Consulting Group (“BCG”).  Prior to rejoining BCG, Ms. Mosconi was the Executive Advisor to the CEO of Synchrony Financial (“Synchrony”), a Fortune 200 consumer finance services company, and also served as Synchrony’s Executive Vice President, Chief Strategy Officer, where she led Strategy, M&A, Ventures and Strategic Partnerships and was responsible for defining and developing Synchrony’s long-term strategic plan. Prior to Synchrony, Ms. Mosconi was a Managing Director and global leader in BlackRock’s Financial Markets Advisory Group. Ms. Mosconi previously spent nearly 20 years as a senior-level Partner at both BCG and McKinsey & Company, where she founded and grew multiple professional services practices in strategy, operations and technology.

Mr. Perry joined Broadridge in 2014 and has served as the Company’s President since 2020. Previously, he served as the Company’s Corporate Senior Vice President, Global Sales, Marketing and Client Solutions from 2014 to 2020. Mr. Perry leads Broadridge’s overall growth strategy, revenue and profitability along with overseeing the Company’s international expansion, corporate development and impact activities globally. He is responsible for Broadridge’s top clients and partners, and for delivering the Company’s annual sales targets across all Broadridge’s businesses and product lines. Prior to joining Broadridge, Mr. Perry spent 14 years at Thomson Reuters, where he held numerous management and commercial roles within risk management, governance and compliance, pricing, sales and account management. Mr. Perry also serves on the board of directors of Verisk Analytics, Inc. and the Financial Services Institute and is an advisory director and past chair of the board of BritishAmerican Business.

“I am honored to join the Board of Directors at Broadridge, a company at the forefront of defining how technology, data and AI are modernizing critical market infrastructure across capital markets and investor services,” said Ms. Mosconi.  “I look forward to working with the management team and the Board to support the company’s continued leadership in driving innovation, strategic growth and long-term value creation across the industry.”

“It is an incredible honor to be appointed to the Broadridge Board alongside an incomparable group of industry leaders and executives,” said Mr. Perry. “I am excited to expand my role at Broadridge as we continue to drive our long-term growth.”

Mr. Perry will not receive any additional compensation in connection with his role as a Board member, and he will not serve as a member of any of the Company’s three standing Board committees, which are comprised solely of independent directors.

About Broadridge

Broadridge Financial Solutions (NYSE: BR) is a global technology leader with trusted expertise and transformative technology, helping clients and the financial services industry operate, innovate, and grow. We power investing, governance, and communications for our clients – driving operational resiliency, elevating business performance, and transforming investor experiences. Our technology and operations platforms process and generate over 7 billion communications annually and underpin the daily average trading of over $15 trillion in equities, fixed income, and other securities globally. A certified Great Place to Work®, Broadridge is part of the S&P 500® Index, employing over 15,000 associates in 21 countries.

For more information about us, please visit www.broadridge.com.

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SOURCE Broadridge Financial Solutions, Inc.

Recreational drug use is a strong, independent predictor of worse one-year cardiovascular outcomes in patients with acute coronary syndrome (ACS), significantly in those with STEMI, according to results from the ADDICT-ICCU study published Jan. 23 in JACC: Advances.

The observational cohort study screened all intensive cardiac care unit (ICCU) patients at 39 centers across France via prospective urinary testing and tracked subsequent major adverse cardiovascular events (MACE; cardiovascular death, nonfatal MI or stroke) through clinical visits and other direct contact.

During screening, 96 (13.5%) of 712 ACS patients (mean age 64 years; 26% women; 57% NSTEMI, 43% STEMI) tested positive for recreational drugs. The most common were tetrahydrocannabinol (n=86), followed by opioids (n=34), stimulants (n=18) and depressants (n=8), with some patients testing positive for multiple substances. Drug detection could not differentiate between acute or chronic drug use, nor were patients tested during follow-up.

Results at one year showed that MACE occurred in 7.0% (n=50) of all patients, including 13% of drug-positive patients vs. 6% of drug-negative patients. Multivariable Cox analysis confirmed that recreational drug use was an independent predictor of MACE (hazard ratio [HR], 2.70; p=0.013). This was a significant association solely in STEMI patients (HR, 4.11; p=0.005), confirmed through propensity matching (HR, 3.39; p=0.022).

“Our findings establish recreational drug use as a clinically meaningful prognostic factor in ACS and underscore the importance of incorporating drug use history into risk assessment,” write study authors Michael Aboujaoude, MD, PhD candidate; Théo Pezel, MD, et al. “Furthermore, these results highlight the potential long-term effects of drug use indicating a need for continued monitoring of recreational drug use within at least [one] year following discharge from the ICCU.”

The US’s closely watched jobs report will once again be delayed, the Bureau of Labor Statistics (BLS) announced on Monday, amid a government shutdown.

The January 2026 jobs report, originally scheduled to be released on Friday, will be rescheduled when federal funding resumes. Data collection for the report has been completed, but the shutdown has forced a delay to releasing the report, which will provide crucial jobs data on the US labor market following the weakest year for job growth since 2020, with the addition of only 584,000 jobs in 2025 compared with 2 million in 2024.

“The Employment Situation release for January 2026 will not be released as scheduled on Friday, February 6, 2026. The release will be rescheduled upon the resumption of government funding,” Emily Liddel, associate commissioner of the BLS, said in a statement.

The Bureau of Labor Statistics has already been faced with significant delays and setbacks resulting from the longest federal government shutdown in US history, 43 days in October and November.

Federal funding lapsed on Sunday following a standoff in Congress over restrictions on Immigration and Customs Enforcement following the killings of two 37-year-old US citizens by federal agents last month. Democratic senators are refusing to vote for a bill authorizing continued spending by the Department of Homeland Security (DHS), demanding the bill be rewritten to include new restrictions and guardrails on ICE agents.

On Friday, the Senate passed five separate measures to fund government agencies through September and a two-week funding bill for DHS, which must be voted on in the House.

House Democrats have so far not guaranteed the votes to pass the funding measure.

The Republican House speaker, Mike Johnson, claimed that House Republicans had enough votes on their own to reopen the government by Tuesday.

The emerging UN carbon market under Article 6.4 of the Paris Agreement, Pacm, saw slow progress last week on draft rules for carbon removals accounting, as experts tasked with working on new Pacm methodologies convened at the UN climate arm’s headquarters in Bonn, Germany.

The panel made some decisions on the so-called reversal assessment tool, which aims to determine the number of Pacm credits to contribute to the market’s reversal risk buffer account, acting as a form of insurance for removal projects.

The tool will help calculate individual risk factors, combined risks and the reduction in reversal risk factors, based on any remediation measures implemented by activity proponents. A buffer factor, expressed as a percentage of credited carbon, would then be calculated, depending on the choices made by project proponents. The higher the percentage, the more credits must be set aside for an activity.

The panel will also determine specific activity risks, with an initial focus on forest carbon storage, geological carbon storage and biochar.

These are not only the most prevalent removal activities in the carbon market, but also dominate those transitioning from Pacm’s precursor, the Clean Development Mechanism (CDM). The panel and the Article 6.4 supervisory body were tasked by countries at the UN Cop 30 climate summit in Brazil in November with prioritising CDM transitions. The panel will consider other types of removal activities at a later stage.

More progress was made last week on the draft rules for renewable electricity generation, on which the panel released a draft methodology for supervisory body approval. It would become the second approved Pacm methodology, if adopted.

The first methodology for generating carbon credits, on flaring or use of landfill gas, is regarded as substantially stricter than its CDM predecessor. Pacm’s downward adjustment factor ensures that baseline emissions decline more significantly over time than under the CDM.

South Korea-based carbon project developer Ecoeye said under the Pacm landfill gas methodology, flaring-only projects carried out in host countries outside least developed countries are likely to experience a 52–76pc reduction in credited emission reductions, compared with CDM-based estimates, over a five-year period, while for electricity generation and heat production it projects a 34–42pc reduction.

The potential third Pacm methodology to be adopted, on clean cooking, considers new submissions while carrying over some elements from an existing CDM methodology.

Another methodology under consideration, on nitrous oxide abatement from nitric acid production, might also see a draft proposal at the next expert panel meeting in March.

Six new Pacm methodologies in total are under consideration. The latest entry is on fertiliser production with renewables-based ammonia, for which a call for public input closed on 27 January. The panel is considering merging this methodology — the development of which was financed by the Germany-supported International Hydrogen Ramp-Up Programme — with another for ammonia production through electrolysis.

By Chloe Jardine

In patients with advanced HER2-positive gastroesophageal adenocarcinoma, treatment with the bispecific antibody zanidatamab-hrii and chemotherapy, with or without the PD-1 inhibitor tislelizumab-jsgr, reduced the risk of disease progression or death by 35% over trastuzumab plus chemotherapy in the phase III HERIZON-GEA-01 trial presented as a late-breaker at the 2026 ASCO Gastrointestinal Cancers Symposium.¹

“This is the first phase III study in advanced gastroesophageal adenocarcinoma to demonstrate a median progression-free survival that is more than 1 year and a median overall survival of more than 2 years,” said Elena Elimova, MD, of Princess Margaret Cancer Centre in Ontario, Canada. The study is also the first to show a benefit for a novel HER2-targeted therapy compared to trastuzumab as part of a combination regimen in the first-line setting, she added.

Compared to treatment with trastuzumab plus chemotherapy, patients receiving a zanidatamab-containing regimen experienced an absolute 4-month improvement in progression-free survival and a 7-month improvement in overall survival, she noted.

At a press briefing, Dr. Elimova commented that the findings indicate zanidatamab is the preferred HER2-targeted agent, over trastuzumab, for HER2-positive locally advanced or metastatic gastroesophageal carcinoma. As for the PD-L1 agent, based on the findings she said she would “feel absolutely comfortable” giving tislelizumab.

Zanidatamab Addresses Treatment Need

As Dr. Elimova pointed out, approximately 20% of patients with gastroesophageal adenocarcinoma (including cancers of the stomach, gastroesophageal junction, and esophagus) have tumors that are HER2-positive and need novel HER2-directed strategies to improve outcomes. With current therapies, outcomes in this population remain “modest,” she said, with a median progression-free survival of around 10 months and a median overall survival of around 20 months. For more than a decade, the standard front-line treatment for HER2-positive metastatic gastroesophageal adenocarcinoma has been trastuzumab plus chemotherapy. For patients whose tumors are PD-L1–positive, pembrolizumab is now a standard component, based on results of KEYNOTE-811.² Relapse within 1 year is still common, however.

Zanidatamab, which is approved in metastatic biliary tract cancer, is a dual HER2-targeted bispecific antibody that binds to two distinct sites on HER2. This binding leads to the crosslinking of neighboring HER2 proteins and receptor clustering on the cell surface. Its multiple mechanisms of action include enhanced HER2 internalization, reduced downstream signaling, and immune-mediated cytotoxicity. Tislelizumab, which is approved in PD-L1–positive metastatic gastric and gastroesophageal junction cancer, is a high-affinity immune checkpoint inhibitor targeting PD-1.

About HERIZON-GEA-01

The study enrolled 914 patients with unresectable, locally advanced, recurrent or metastatic gastroesophageal adenocarcinoma; more than two-thirds had gastric cancer. They had no prior treatment in this setting and no prior HER2-targeted therapy or immunotherapy in any setting. They were randomly assigned 1:1:1 to receive trastuzumab plus chemotherapy (Arm A); zanidatamab every 3 weeks plus chemotherapy (Arm B); or zanidatamab every 3 weeks plus tislelizumab every 3 weeks plus chemotherapy (Arm C). CAPOX (capecitabine, oxaliplatin) was the chemotherapy choice for 90% of patients. The dual primary endpoints were progression-free survival by blinded independent review and overall survival. 

Key Findings

In this interim analysis, both zanidatamab-containing regimens led to clinically meaningful and statistically significant prolongation of progression-free survival vs trastuzumab plus chemotherapy, yielding more than 4 additional months free from worsening disease. The benefits were generally observed across key subgroups, including geographic region and PD-L1 status (Table).

Zanidatamab plus tislelizumab and chemotherapy also demonstrated a statistically and clinically meaningful overall survival benefit, providing 7 months longer median overall survival, whereas zanidatamab plus chemotherapy showed a trend (Table). “At this interim analysis, overall survival data were immature for zanidatamab plus chemotherapy, but there was a strong trend favoring it, with a 5-month longer median overall survival seen,” said Dr. Elimova. The trial is ongoing, and additional overall survival analyses are planned for this arm.

Responses were deeper and more durable in the zanidatamab-containing arms as well. Confirmed objective response rates and complete response rates were 69.6% and 17.1%, respectively, with zanidatamab plus chemotherapy; 70.7% and 19.6%, respectively, with zanidatamab plus tislelizumab plus chemotherapy; and 65.7% and 11.0%, respectively, with trastuzumab plus chemotherapy. Median duration of response was 14.3, 20.7, and 8.3 months, respectively.

While the outcomes were more striking with the zanidatamab plus tislelizumab and chemotherapy regimen, Dr. Elimova reminded journalists at the press briefing that PD-L1 positivity is currently a requirement for treatment with an immune checkpoint inhibitor.

What is unique in the HERIZON-GEA-01 study is that patients benefited from zanidatamab plus tislelizumab and chemotherapy regardless of PD-L1 status.

Safety Profile

The safety profile was consistent with the known safety profile of each individual agent. Grade ≥ 3 treatment-related adverse events were reported for 59.0% of the zanidatamab plus chemotherapy arm, 71.8% of the zanidatamab plus tislelizumab plus chemotherapy arm, and 59.6% of the trastuzumab plus chemotherapy arm. Discontinuations due to treatment-related toxicities were noted for 34.4%, 42.5%, and 29.1%, respectively.

Diarrhea was the most common treatment-related toxicity in all three arms. It generally occurred early in treatment and resolved within 3 weeks, and discontinuation of HER2-targeted therapy due to this was infrequent.

DISCLOSURE: Dr. Elimova had personal financial disclosures for Merck (family member), BMS, Zymeworks, Daiichi Sankyo/AstraZeneca, Roche Canada, AbbVie, Astellas Pharma, BeOne, Signatera, Amgen, Jazz, Novartis, and Viracta Therapeutics.

REFERENCES

1. Elimova E, Rha SY, Shitara K, et al. Zanidatamab + chemotherapy ± tislelizumab for first-line HER2-positive locally advanced, unresectable, or metastatic gastroesophageal adenocarcinoma: Primary analysis from HERIZON-GEA-01. 2026 ASCO Gastrointestinal Cancers Symposium. Abstract LBA285. Presented January 8, 2026.

2. Janjigian YY, Kawazoe A, Bai Y, et al: Final overall survival for the phase III, KEYNOTE-811 study of pembrolizumab plus trastuzumab and chemotherapy for HER2+ advanced, unresectable or metastatic G/GEJ adenocarcinoma. ESMO Congress 2024. Abstract 1400O. Presented September 14, 2024.

 

EXPERT POINT OF VIEW

ASCO Expert Rachna Shroff, MD, Associate Director of Clinical Investigations and co-leader of the Gastrointestinal Clinical Research Team at the University of Arizona Cancer Center, shared her thoughts on the findings of the phase III HERIZON-GEA-01 trial.

“What I think is remarkable about this study is to see a duration of response that is truly meaningful: 20 months with zanidatamab and chemotherapy, in addition to a positive benefit in progression-free survival and at least a trend toward an overall survival benefit with just zanidatamab and chemotherapy,” Dr. Shroff said.

“The findings have the potential to be practice-changing,” Dr. Shroff said. Current NCCN Guidelines recommend chemotherapy plus trastuzumab alone or with pembrolizumab in PD-L1–positive patients, a regimen approved by the U.S. Food and Drug Administration in March 2025 based on the KEYNOTE-811 data.² “What seems clear is that zanidatamab can, and should, be a new HER2-targeting agent for upper GI cancers.”

According to Dr. Shroff, the most impactful results were observed when the immune checkpoint agent tislelizumab was added to the regimen. “Keep in mind that this study was designed before the KEYNOTE-811 data were available, so the arm of zanidatamab plus tislelizumab and chemotherapy is obviously very relevant as we think about adding immunotherapy to HER2-targeting” in patients whose tumors are PD-L1–positive. 

In KEYNOTE-811, at a median follow-up of 50.2 months, pembrolizumab added to trastuzumab and chemotherapy in the first-line setting led to a median overall survival of 20.0 vs 16.8 months for placebo plus trastuzumab and chemotherapy (hazard ratio, 0.80; P = .0040). The 36-month overall survival rate was 28% with pembrolizumab and 23% with placebo.

Further commenting to The ASCO Post, Dr. Shroff added, “Cross-trial comparisons are difficult but the median overall survival from HERIZON with the zanidatamab/tislelizumab/chemotherapy combination was over 2 years. While we will need to see longer-term follow-up and better understand the impact based on PD-L1 status, this combination looks promising. Given the overall survival benefit now seen with chemotherapy plus zanidatamab plus tislelizumab, this combination could likely become the new standard of care, with the recognition that it has not been compared head-to-head with the KEYNOTE-811 combination.”

DISCLOSURE: Dr. Shroff had personal disclosures for AstraZeneca, Boehringer Ingelheim, Boston Scientific, Genentech, Ipsen, Merus, and Regeneron.

REFERENCE

1. Janjigian YY, Kawazoe A, Bai Y, et al: Final overall survival for the phase III, KEYNOTE-811 study of pembrolizumab plus trastuzumab and chemotherapy for HER2+ advanced, unresectable or metastatic G/GEJ adenocarcinoma. ESMO Congress 2024. Abstract 1400O. Presented September 14, 2024.

Introduction

Background

The digital transformation of health care has been driven by the integration of telemedicine, mobile health (mHealth) applications, electronic health records, and wearable devices, which have significantly reshaped the delivery of medical services. These innovations address the limitations of traditional care models, which often struggle to meet the evolving demands of health care, particularly for aging populations in rural or underserved areas []. By improving access to care, supporting chronic disease management, and promoting preventive health care initiatives, digital health technologies offer promising solutions.

Notably, older adults, who often face mobility limitations, chronic illnesses, and restricted access to traditional health care services, are likely to gain substantial benefits from these digital health innovations [,]. However, despite the potential benefits, older adults remain among the most resistant groups to adopting these technologies []. This reluctance is widely documented in prior research and often attributed to multiple factors, including limited digital literacy, usability concerns, lower self-efficacy, privacy concerns, and a strong preference for in-person health care interactions. These barriers contribute to older adults’ limited willingness to engage with digital health care solutions [-].

The persistence of this reluctance suggests that adoption-centric models may offer an incomplete explanation, highlighting the need for complementary resistance-focused frameworks. To better understand these patterns, we first reviewed established technology adoption models used in health care, clarifying their scope and limitations, and then introduced innovation resistance theory (IRT) as a complementary resistance-focused framework.

Existing Technology Adoption Models

Established theoretical models, such as the technology acceptance model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), have been widely used to explain individuals’ adoption and use of new technologies []. These models highlight factors such as perceived usefulness, ease of use, performance expectancy, and social influence as key determinants of technology adoption [,]. Complementary to these, Rogers’ Diffusion of Innovations Theory describes how new technologies spread through populations by considering factors such as adopters’ characteristics, communication channels, and social systems []. These frameworks have been extensively validated and remain central tools for understanding and measuring technology acceptability and usage intentions in health care.

In the context of older adults’ digital health use, adoption-focused models provide valuable insights into factors associated with acceptance and initial uptake; however, prior literature suggests that older adults’ persistent nonuse and resistance are also shaped by affective, psychological, and contextual factors that are not always represented as central constructs in these models []. For example, a scoping review by Wilson et al [] applied UTAUT2 as an analytic framework to map barriers and facilitators to eHealth use among older adults. They identified gaps in the evidence base for certain UTAUT2 constructs (eg, habit and hedonic motivation) alongside recurring concerns related to privacy, trust, and support needs []. Another empirical study showed that older adults’ intention to use mHealth was not explained solely by perceived ease of use and perceived usefulness, with person-related, technology-related, and contextual barriers influencing adoption []. Fox and Connolly further argue that research on older adults’ resistance to mHealth remains limited and therefore examine how privacy concerns, trust, and risk beliefs influence willingness to adopt beyond standard adoption-model constructs []. Taken together, these findings suggest that complementing adoption-focused models with resistance-oriented frameworks may better capture why some older adults actively avoid digital health technologies, including perceived risks, emotional discomfort, and contextual constraints [,-]. Accordingly, adoption-focused models may emphasize intention and perceived benefits, whereas nonadoption can also reflect an active decision-making process shaped by perceived risks and psychological discomfort. Therefore, we propose complementing adoption-focused theories with a resistance-oriented framework, such as IRT.

Innovation Resistance Theory (IRT) as a Conceptual Framework

IRT, introduced by Ram and Sheth [], was developed to understand consumer resistance to marketing innovations and their behavior. Unlike models that emphasize adoption facilitators, IRT focuses on understanding why individuals hesitate or actively refuse to adopt new products, services, and ideas, even when they offer potential benefits []. The strength of IRT lies in its focus on perceived barriers rather than enablers, making it well-suited for populations such as older adults, where complex emotional, cognitive, and contextual factors influence nonuse. By focusing on the barriers, IRT offers a different perspective that shifts attention from the characteristics of innovations themselves to the reasons behind consumer reluctance to adopt them, especially when such adoption threatens established habits and routines or involves perceived risks [-]. In this view, resistance is not merely a lack of adoption but an active process that focuses on barriers to acceptance, including functional, psychological, and social resistance factors [].

Resistance is defined as a multidimensional construct encompassing 3 dimensions: cognitive resistance, which involves individuals’ appraisal of innovations and their perceived risks; affective resistance, which stems from emotional responses such as fear, frustration, or anxiety; and behavioral resistance, which manifests in actions ranging from passive disengagement to active opposition [,]. Within the IRT, these dimensions are further classified into functional and psychological barriers. Functional barriers include the usage barrier, which reflects the extent to which an innovation is perceived as requiring changes to established routines or habits; the value barrier, which arises when the individual perceives that the benefits of an innovation do not outweigh its costs; and the risk barrier, which represents concerns about the financial, functional, and social consequences of adopting an innovation.

Psychological barriers encompass traditional barriers, which refer to the degree to which an innovation forces an individual to accept changes that challenge cultural norms or long-standing behaviors, and image barriers, which relate to the degree to which an innovation is perceived as having an unfavorable image or negative associations [,]. These psychological categories often reflect deeper symbolic concerns, such as identity, generational belonging, or perceived legitimacy of digital care. This classification allows IRT to capture the multifaceted nature of resistance in older populations, particularly their emotional unease, normative preferences, and experiential distrust of digital systems. By categorizing resistance into functional and psychological barriers, IRT may provide a comprehensive framework for understanding why older adults struggle to adopt digital health solutions.

Over time, IRT has gained strong empirical support across different service and technology contexts. For example, in mobile banking research across Thailand and Taiwan, IRT barriers explained 60%‐66% of the variance in resistance intentions, with usage, value, risk, and image barriers showing statistically significant effects []. In a large Italian survey, Spinelli et al [] showed that usage barriers and value-related concerns significantly reduced both actual mobile payment use and intention to adopt, whereas risk and image barriers had weaker or nonsignificant effects, and their impact varied across technology-readiness clusters []. Similarly, a study of Internet and mobile banking in Finland found that the value barrier was the dominant inhibitor of adoption and intention to adopt, while image and tradition barriers differentiated postponers from rejecters across seemingly similar service innovations [].

Together, these findings demonstrate that IRT-based barriers have substantial explanatory power for resistance to digital innovations. Therefore, in this review, we apply IRT to structure the evidence on older adults’ resistance toward digital health technologies and to examine whether the identified resistance factors map onto, extend, or refine the original IRT barrier categories. The aim of this scoping review was to synthesize and conceptualize evidence on older adults’ resistance to digital health technologies in primary care using IRT. Specifically, we aimed to identify and categorize resistance factors into IRT functional and psychological barriers and to examine how these barriers co-occur and interact to inform a conceptual model of resistance. The review was guided by the following research questions: (1) What is known from the existing literature about older adults’ resistance to using digital health technologies for monitoring and management in primary health care? (2) What are the functional (usage, value, risk) and psychological (tradition, image) IRT barriers reported across studies? (3) How do IRT barriers co-occur and link within and across studies?

Methods

Study Design

The methodology for this scoping review follows the framework proposed by Arksey and O’Malley [], incorporating refinements by Levac et al [], and the Joanna Briggs Institute (JBI) Reviewers’ Manual []. We selected the scoping review approach to explore the current body of knowledge regarding older adults’ resistance to digital health technologies through the lens of IRT, as it is well-suited to mapping the existing literature, identifying and interpreting patterns of functional and psychological resistance across heterogeneous study types. Within this design, our goal was to provide a theory-informed synthesis that evaluates how well IRT accounts for older adults’ resistance to digital health in primary care and to identify conceptual and empirical gaps that warrant further investigation and measurement development. The reporting of this scoping review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines []. Reporting of the search methods followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension checklist (PRISMA-S) [] to ensure transparent and complete reporting. The completed PRISMA-ScR checklist is provided in , and the PRISMA-S checklist is provided in .

Stage 1: Identifying the Research Question

The review was guided by predefined research questions (presented at the end of the Introduction section) informed by IRT and scoping review guidance.

Stage 2: Searching and Identifying Relevant Studies

A literature search was conducted across 5 major databases: PubMed, CINAHL, Ovid Medline, Web of Science, and Scopus, to identify peer-reviewed publications relevant to the research question. These databases were selected for their broad coverage of health, behavioral, and interdisciplinary studies on older adults and digital health. Each database was searched separately through its web interface, and all retrieved records were exported to Mendeley (version 1.63.0; Mendeley Reference Manager) for deduplication. Review studies were not included in this scoping review; however, their reference lists were screened to identify potentially eligible primary studies. No study registries were searched. Apart from reference-list screening, no additional sources were searched, and no citation searching was undertaken. We did not contact authors to identify additional studies, and no other search methods were used beyond those described. We did not use any previously validated search filters. Search strategies were developed specifically for this scoping review by the authors and were not peer reviewed by an independent expert before execution. We did not adapt or reuse search strategies from previous literature reviews for any substantive part of our search.

The search was carried out on December 20, 2024, and was rerun on November 18, 2025, to identify newly published studies since the initial search. The search followed the JBI PCC structure (Participants, Concept, Context) and combination of the following keywords and MeSH terms: “older adults,” “elderly,” phenomena of “digital health,” “eHealth,” “Telemedicine,” and context of “primary health care,” and “barriers to health technology.” Boolean operators were used to combine search strings (eg, AND, OR). Title and abstract screening and full-text review were conducted by 2 independent reviewers (YB and RTS). The search strategy and keyword combination can be found in . Additionally, reference lists of included studies were manually screened to identify relevant studies not captured in the initial searches.

Stage 3: Selecting the Relevant Studies

Inclusion and Exclusion Criteria

The review included papers that met predefined inclusion and exclusion criteria aligned with the JBI PCC framework for scoping reviews ().

The context of the review centered on the resistance to digital health within the framework of IRT. Publications addressing the use of digital health within the resistance domains of usage, value, risk, traditional, and image barriers were considered, while those focusing solely on the description of digital health adoption and facilitators were excluded. Also, no minimum sample size threshold was applied. Consistent with the objectives of a scoping review, studies were eligible regardless of their sample size to maximize coverage of designs (qualitative, quantitative, mixed methods) and contexts.

Study Selection Process

The studies were screened against the inclusion and exclusion criteria developed by the authors. The selection process followed three steps: (1) Title and abstract screening to remove irrelevant or duplicate records; (2) Full-text review based on predefined inclusion and exclusion criteria; and (3) Final inclusion based on relevance for examination of resistance to digital health technologies among older adults.

A total of 4976 records were identified through database searches, and 2387 duplicates were removed. After screening 2589 titles and abstracts, 227 full-text articles were reviewed. Two independent reviewers (YB and RTS) evaluated relevant publications for eligibility and selected qualifying publications based on the inclusion/exclusion criteria. We used a consensus-based approach, prioritizing unanimous agreement through re-evaluation of the eligibility criteria; if consensus could not be reached, a third reviewer would adjudicate. An initial pilot screening was conducted independently by both reviewers to ensure consistent interpretation of the eligibility criteria. Discrepancies identified at this stage were resolved through discussion and used to refine the criteria, resulting in full agreement during subsequent screening. A total of 17 studies met the inclusion criteria and were included in the final synthesis. A PRISMA flow diagram illustrates the selection process.

Stage 4: Charting the Data – Data Extraction and Synthesis

Two authors independently extracted data from all included studies. Data were charted using a standardized extraction form developed for this review, capturing study design, aims, population, type of digital health intervention, and resistance-related findings. Using a concept-driven thematic synthesis, findings were organized into 5 resistance categories from the IRT: usage, value, risk, tradition, and image barriers. A structured matrix was used to map resistance dimensions across the studies. Barrier statements were first open-coded descriptively and then mapped to one IRT family using prespecified rules. Data charting was conducted by the 2 authors, and disagreements were resolved by consensus.

Stage 5: Collating, Summarizing, and Reporting the Results

Findings were organized in three layers: (1) mapping of the evidence base (study characteristics, settings, modalities), (2) concept-driven qualitative synthesis using IRT classification (usage, value, risk, tradition, and image), and (3) relational integration examined interconnections across IRT barriers. We extracted and coded barrier co-occurrences and linkages reported in the studies’ results sections and participant quotations when two or more barriers were described as co-occurring or interacting. Links were considered explicit when directly stated, inferential when implied within a study’s narrative context, and integrative when consistent patterns recurred across multiple studies (worked examples are provided in the Results).

Results

General Characteristics of the Studies

The database search initially identified 4976 records. After removing duplicates, screening titles and abstracts, and full papers, 17 studies were included in the final synthesis (). The included papers represent a predominantly high-income Western countries from the United States (n=4), Sweden (n=3), the Netherlands (n=3), Canada (n=2), Finland (n=1), Norway (n=1), and the United Kingdom (n=1), with only 2 studies from non-Western settings Israel (n=1) and Indonesia (n=1). Eleven studies were qualitative, 3 studies were cross-sectional, and 4 studies were mixed methods designs. Sample sizes ranged from 11 to over 4500 participants, though qualitative samples were generally smaller and in-depth. In terms of digital health modalities, most studies focused on telemedicine or digital consultations (12/17) and patient portals or eHealth services (8/17), with comparatively few studies examining mobile apps or tablets (3/17) and wearables or remote monitoring (2/17) ().

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The IRT framework was used to guide the coding of extracted findings into the 5 barrier domains (usage, value, risk, tradition, and image).

The findings synthesis is presented in the following sections and summarized in and .

details the barriers identified by each study, presenting a matrix that maps each study to the usage, value, risk, tradition, and image barriers. defines each barrier subcategory and summarizes how these resistance themes were operationalized across the studies.

Functional Barriers

In the context of IRT, functional barriers refer to resistance stemming from the practical and objective attributes of the innovation itself, including its required usage, perceived value, and associated risks [].

Usage Barriers

Usage Barriers were the most consistently reported resistance factor, found in 16 studies. Older adults face significant usage barriers to adopting digital health technologies, largely due to technical challenges, usability difficulties, and concerns about quality of care. A central theme across studies was interface complexity. Many participants described digital health platforms as confusing, unintuitive, and poorly designed. Common challenges included unclear layouts, unintuitive menus, and multi-step authentication processes requiring repetitive actions such as logging in, remembering passwords, and uploading medical documents [-,,,,,]. These features increased cognitive load and made even basic digital interactions feel burdensome and prone to mistakes.

The difficulties were compounded by technology usability issues linked to age-related cognitive and sensory impairments. Older adults with a decline in vision, hearing loss, or memory difficulties and reduced fine motor skills struggled with small font sizes, poor audio quality, poorly structured information, and touchscreen sensitivity, which makes many applications inaccessible without assistance [,,,,]. In addition, language and terminology complexity emerged as a significant obstacle. Technical jargon or unfamiliar medical terms often made it difficult for users to interpret instructions or understand the content presented on-screen, particularly among those with limited formal education or health literacy [,,,].

Another recurring issue was the digital learning curve. Older adults reported limited prior experience with digital health tools or services and found it challenging to adapt to new systems [,,-,]. This often led to a self-efficacy deficit where individuals doubted their ability to complete digital health tasks independently. These doubts fueled hesitation and reinforced a sense of digital exclusion, leading to frustrations, avoidance behaviors, and a greater need for support before successfully adopting telemedicine tools [-,,]. Closely related to this was technology anxiety, the fear of making mistakes or causing harm through improper use, which discouraged many from engaging with telemedicine platforms.

Concerns about system reliability and uncertainty about using digital health care tools make older adults feel less confident in their technical abilities and unprepared [,,,,,,,], leading to avoidance behaviors, where they opt not to engage with digital health solutions to minimize the risk of errors [,,].

Beyond usability concerns, preadoption resistance arises from changes in communication dynamics within digital health care. In contrast to traditional face-to-face consultations, which allow patients to express multiple health concerns in a single visit and rely on nonverbal cues, digital health platforms, particularly telemedicine services, alter this dynamic. Studies showed that when older adults use digital health services, they struggle to articulate their symptoms or find it difficult to understand medical terminology or provider explanations [,]. As a result, they hesitate to fully communicate medical concerns, whether typing them into digital platforms or discussing multiple health issues during digital visits. This contributes to a perception that digital care is less effective than in-person care [-], further discouraging older adults from fully embracing digital health technologies.

Value Barriers

Value barriers to adopting digital health solutions among older adults primarily stem from informality bias, the perceived lack of necessity of digital tools, concerns about care quality, and misalignment between the effectiveness of available digital health care services and patient expectations [,,]. While many acknowledge that telemedicine may be appropriate for minor health issues and routine follow-ups, they often do not view it as an adequate substitute for in-person consultations. This limited use perception is particularly strong when it comes to complex conditions that require physical examination or long-term management [,,-,,,].

Skepticism about the effectiveness of remote consultations is a common concern. Many older adults feel that digital platforms fail to capture nonverbal cues, which are essential for accurate medical assessment and effective patient-provider communication. This concern is particularly pronounced among individuals managing chronic illnesses, who consider ongoing physical evaluations and in-person interactions with health care professionals to be vital components of proper care []. Moreover, older adults often emphasize the importance of relational continuity with their health care providers, an aspect they feel is disrupted and compromised in digital health environments. Telemedicine is frequently perceived as impersonal and transactional, lacking the trust and emotional support that typically characterize in-person visits, qualities that many older adults highly value in primary care settings [,,,]. As a result, some individuals refuse to see their providers outside of traditional clinical settings, which further reinforces resistance to digital health solutions [,].

Beyond concerns about quality of care, many older adults also question the necessity of digital health interventions, particularly when the current health care system meets their needs effectively [,]. Some dismissed telemedicine as a “solution for a nonexisting problem,” believing that traditional in-person visits provide sufficient care without the added complexity of digital tools [,,]. This skepticism is often exacerbated by low digital literacy or past negative experiences with digital health technology, leading many to view telemedicine and digital health apps as unnecessary, ineffective, or not worth the effort required to learn and adapt []. When the perceived benefits of digital health do not outweigh the effort and risks associated with adoption, resistance to these solutions remains strong.

Risk Barriers

Risk barriers to digital health adoption among older adults primarily revolve around concerns about diagnostic uncertainty and the potential of missed health issues due to the absence of physical examinations, body language, and other visual cues essential to accurate clinical assessment [,-]. Many older individuals worry that the lack of hands-on evaluation in telemedicine could lead to overlooked symptoms or misinterpretations by health care providers. A prominent concern is technology misuse anxiety, which arises from fear of making errors during digital interactions. Participants described anxiety about technical distractions, errors in digital documentation, incomplete data entry, and uncertainty about whether submitted information, such as messages, forms, or test results, would be properly received and understood by their health care team [,,]. These apprehensions are linked to fears of miscommunication with health care providers, incorrect medical decisions, or overlooked health conditions [-,,].

Beyond diagnostic concerns, older adults express privacy and security concerns. There is a common distrust of the integrity and security of digital health platforms [,,-,,,], particularly related to fear that personal health data could be exposed to unauthorized access, fraud, or misuse. Some participants described concerns about scams that mimic legitimate digital services, increasing their reluctance to trust or engage with digital health tools []. This skepticism is further compounded by uncertainty around how health care institutions collect, store, and share data through electronic health records and patient portals [].

Additionally, lack of confidence in digital skills was repeatedly cited as a major factor behind misuse anxiety. Older adults often lack confidence in their digital skills, particularly in navigating complex interfaces or troubleshooting technical issues. Common fears included accidentally deleting important information, misunderstanding medical results, or failing to complete critical health care tasks [,]. As a result, many preferred to avoid digital health services entirely rather than risk making mistakes that could negatively impact their care.

Another key source of resistance is the perceived loss of autonomy in health care decision-making. Some older adults expressed concerns that eHealth solutions shift decision-making control from patients to automated systems, reducing their ability to advocate for personalized care and communicate effectively with health care providers about their health care [,]. This fear is particularly prevalent among those unfamiliar with electronic health records or unaware of how to use digital clinical discussions.

Psychological Barriers

Psychological barriers refer to resistance stemming from subjective, cognitive, and emotional conflicts between the innovation and the individual’s established traditions and self-image barriers [].

Tradition Barriers

Traditional barriers to digital health adoption among older adults arise from long-established care routines, personal preferences for in-person interactions, and a strong need for familiarity in health care interactions. Many older adults have their health-seeking behaviors around face-to-face consultations, expressing satisfaction with traditional care models and questioning the necessity or value of digital alternatives [,,]. They often perceive little incentive to switch to eHealth services when current systems already meet their expectations [,,]. A central theme is the belief that in-person interactions offer superior quality of care, stronger provider-patient relationships, and greater emotional warmth. Digital platforms are often seen as impersonal, lacking the human touch and nonverbal communication cues that older adults consider essential for effective medical consultations [,-,,,,-]. This is especially concerning for individuals managing chronic conditions or complex health issues, where verbal-only communication may be insufficient for accurate symptom reporting and clinical assessment [,].

The need for familiarity in care also contributes to resistance toward digital health adoption. Many older adults prefer continuity with known health care professionals, such as physicians, nurses, or other health care professionals, and value personalized guidance and documentation, such as printed instructions or handwritten over generic digital content. Some do not want all services to be transferred through digital platforms, especially when health care and social service issues are too complex to be handled without face-to-face contact [,,,,].

Another common concern is the perceived legitimacy of telemedicine. Some older adults do not view phone or video consultations as valid medical encounters, describing them as informal and lacking the authority of traditional office visits []. This perception is heightened among individuals who had not used digital health before the COVID-19 pandemic and who experienced the rapid shift to telehealth as both disruptive and disorienting, owing to complex interfaces and limited user guidance []. For these individuals, digital health solutions interfere with familiar health care routines and pose significant adaptation challenges [].

Image Barriers

Image barriers to digital health adoption among older adults arise from negative perceptions of technology, distrust in digital health solutions, and skepticism about their legitimacy and effectiveness in clinical care. Many older adults associate digital health technologies with lower quality of care and consider them as an unacceptable alternative to traditional in-person visits [,]. For some, these technologies are viewed as overly complex, impersonal, and rigid, contributing to a Legitimacy Gap, a perception that digital health care lacks the authenticity, reliability, and interpersonal value of conventional medical interactions [,,]. This skepticism is reinforced by the belief that health care should be hands-on, personalized, and relational, the qualities they feel digital platforms fail to deliver.

Another central issue underlying this perception is the Generational Digital Divide. Many older adults view digital health tools as designed primarily for younger, digitally proficient users, and they report feeling excluded or disadvantaged by their limited experience with digital technologies [,-,]. This belief is often coupled with self-perceived technological inadequacy, where individuals feel “too old” to learn or incapable of using new systems effectively []. These psychological barriers are compounded by negative past encounters with health care bureaucracy or poorly designed interfaces, which foster the impression that digital health prioritizes efficiency over patient-centered care []. Additionally, difficulties navigating eHealth platforms often lead to a sense of powerlessness in managing their health, further alienating them from digital solutions.

Older adults also view telemedicine and digital health as unsuitable for both routine and complex care needs [,,]. Many perceive these technologies as inferior to traditional, in-person medical consultations, citing concerns about their inability to provide thorough physical examinations, comprehensive assessments, and hands-on diagnostics []. Digital health is also associated with social isolation and reduced autonomy, as some fear that shifting toward digital health care may limit direct patient-provider interactions and diminish their role in medical decision-making []. This contributes to a strong preference for traditional care models, where in-person visits provide greater trust, familiarity, and perceived quality.

Evidence and Gap Map

Across the included studies, there was substantial variation in both the types of digital health technologies examined and the specific resistance factors reported. To strengthen the mapping component of this scoping review, we developed an evidence and gap map to summarize the distribution of evidence and identify gaps across digital health modalities and resistance constructs. Guided by IRT, we categorized studies by the type of digital health modality and by IRT-informed barrier subcategories derived from the extracted findings ().

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Specifically, the map highlights that evidence is concentrated in studies of telemedicine and patient portals or eHealth services, with fewer studies addressing mobile apps or tablets and minimal evidence on wearables or remote monitoring. Across modalities, frequently represented barriers included usability and interface complexity, self-efficacy and technology anxiety, and trust-related concerns such as privacy, data security, and perceived legitimacy of digital encounters. In contrast, several modalities-barrier intersections show limited or absent evidence, indicating that resistance to certain technologies, particularly wearables and app-based monitoring, remains underexplored in primary care contexts.

Conceptual Integration: Interconnected Barriers Leading to Digital Health Avoidance

Across the 17 included studies, usage barriers were the most consistently reported (16/17 studies). Risk barriers and tradition barriers were also prevalent (15/17 studies). Value barriers were common (13/17 studies), and image barriers were reported in a smaller, but still substantial subset (11/17 studies). Co-occurrence patterns were apparent across domains, and worked examples illustrate how linkages were derived. For example, one participant described limiting use to familiar functions and avoiding other features, indicating a usage barrier, accompanied by anxiety when stepping outside her comfort zone, suggesting an affective risk component and fear of making mistakes: “I never look over there, I just do everything I have learned… Outside of that, I become nervous.” []. In another study, a participant noted that he did not grow up with technology, indicating a usage barrier related to limited digital familiarity, and expressed a tradition barrier by preferring to arrange appointments by phone and speak with the physician face-to-face rather than use digital channels: “But we did not grow up with the computer. I would rather make a phone call to arrange an appointment and prefer to talk face-to-face to the physician” []. Another participant questioned the adequacy of digital encounters for a proper clinical assessment, reflecting an image or quality concern that co-occurred with a tradition-related preference for face-to-face care and an implied need for greater diagnostic assurance (risk): “I would rather that the doctor can actually touch me, examine me with a stethoscope… I also think in-person communication is sometimes better…” []. Together, these patterns suggest that resistance is rarely attributable to a single factor; rather, studies frequently report clusters of functional and psychological barriers that co-occur. These recurring clusters informed the relational integration step; linkages were coded as explicit when directly stated in study results or participant quotes, inferential when implied through within-study co-occurrence and narrative context, and integrative when synthesized across multiple studies showing consistent patterns.

As part of the relational integration step of our synthesis (Stage 5), we developed a conceptual model that integrates the identified barriers into an interconnected structure (). This conceptual integration was undertaken to move beyond listing individual barriers and to summarize recurring co-occurrence patterns observed across the studies. The interconnected nature of resistance barriers creates a self-reinforcing reaction cycle that leads to avoidance behaviors among older adults. Rather than operating in isolation, functional and psychological barriers interact dynamically, compounding resistance and entrenching disengagement from digital health platforms.

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Technology usability challenges contribute to difficulties in the digital learning curve, which, along with interface complexity, results in a self-efficacy deficit and a lack of confidence in using digital health technologies. This diminished self-efficacy further fuels technology anxiety, increasing hesitation and discouraging engagement. Importantly, these usability issues do not just reduce confidence; they initiate a cascade of psychological barriers that elevate emotional discomfort and cognitive overload. illustrates this cascading effect: a feedback system where usability problems initiate low self-efficacy, which in turn escalates into technology anxiety. This psychological discomfort amplifies risk perceptions, including fear of misdiagnosis, privacy breaches, and technology misuse. These concerns reduce trust in digital health care solutions and reinforce avoidance behaviors. Privacy and security concerns and technology anxiety reinforce each other, creating a cycle of distrust. As the trust in the system diminishes, older adults become less likely to interact with digital platforms, which limits exposure and impedes skill acquisition, further deepening their self-efficacy deficit. This cycle in is illustrated through closed feedback loops, where arrows between barriers represent how one resistance factor amplifies another (eg, Interface Complexity → Low Self-Efficacy → Technology Anxiety → Avoidance).

Traditional barriers, such as a strong preference for in-person care and the need for familiarity, also strengthen image barriers, including the legitimacy gap and the generational digital divide, further discouraging digital health adoption. As shown in , these values-based preferences and generational perceptions reinforce internal skepticism with digital tools, particularly when technology is perceived as impersonal. The legitimacy gap reflects older adults’ perception that digital tools lack the authenticity and authority of face-to-face care, while the generational divide reinforces feelings of exclusion from technologies perceived as designed for younger users. also highlights this convergence between identity-based resistance (eg, tradition/image) and capability-based resistance (eg, usability, anxiety). Together, these interrelated barriers form a self-reinforcing loop, where initial usability difficulties and emotional skepticism amplify resistance, which leads to withdrawal from digital health use entirely ().

Discussion

Principal Findings

This scoping review applied the IRT to examine older adults’ resistance to digital health technologies within primary care contexts. Across the included studies, we found consistent functional barriers (such as usability difficulties, interface complexity, and sensory or cognitive limitations) and recurrent psychological barriers (such as a preference for in-person care and concerns about the legitimacy of digital encounters), with value-related concerns (limited perceived benefit) and risk-related concerns (diagnostic uncertainty, privacy, and security worries) also prominent.

The findings suggest that resistance is not a static failure to adopt nor a passive disengagement, but rather a dynamic, emotionally embedded process. This process is shaped by the interaction of functional and psychological factors, including identity and value-related concerns, which do not operate in isolation but reinforce each other in feedback loops that entrench avoidance behaviors over time. The interplay between usability challenges, emotional discomfort, and value-based misalignment reflects the multifaceted nature of resistance in this population. Also, interrelationships indicated that capability-related barriers erode confidence and increase anxiety, while identity-related concerns reinforce distrust and preference for face-to-face care, together discouraging engagement. Linkages were categorized by evidentiary basis (explicit, inferential, integrative), supporting IRT as a useful framework for organizing and interpreting resistance patterns.

Functional barriers such as interface complexity, digital learning curves, and age-related sensory or cognitive limitations were among the most identified sources of resistance. However, their significance lies not only in their prevalence but in their role as catalysts: they often trigger negative psychological responses, including diminished self-efficacy, anxiety, and fear of error. These emotional reactions contributed to a broader sense of technological vulnerability and led to sustained disengagement, demonstrating how technical design and user experience are deeply interconnected.

Beyond usability, resistance was often rooted in symbolic and identity-related concerns. A preference for face-to-face interactions, generational beliefs regarding technology, and the desire for continuity with known providers were consistently linked to what can be described as symbolic distancing, a form of resistance grounded in perceived legitimacy and personal norms. Even where functionality improved, older adults continued to express skepticism, viewing digital tools as impersonal, exclusionary, or inappropriate for managing complex health needs. This suggests that emotional and symbolic dimensions may play a stronger influence on resistance than previously recognized.

These insights align with earlier theoretical work that repositions resistance as a dynamic, emotionally driven response process. The findings support an evolving theoretical perspective that frames resistance as an active process. Rather than being the inverse of adoption, resistance emerges from distinct cognitive and emotional pathways and may dominate decision-making even in the presence of positive attitudes []. Other research has also shown that tradition and identity-based concerns frequently outweigh usability considerations in shaping innovation rejection, particularly in service-oriented settings []. This review affirms that older adults’ resistance is rarely due to a lack of awareness or rational evaluation alone, but rather reflects deeply embedded emotional and symbolic stances.

Breaking these loops requires targeted interventions that not only simplify interface design but also rebuild self-efficacy, trust, and the perceived legitimacy of digital care. Accordingly, programs should pair practical usability supports (eg, task simplification, assisted-digital options, scaffolded practice) with psychological strategies (eg, anxiety reduction, trust-building, culturally and linguistically responsive framing).

Comparison to Prior Work

The findings of this review both align with and challenge established models of technology acceptance. For instance, it complements the critiques of the extended UTAUT, which has been applied to prior studies involving older adults in health care settings. One study has highlighted effort expectancy, perceived usefulness, and trust in health care providers as primary predictors of adoption. While these factors remain relevant, this review suggests they are insufficient to fully account for persistent resistance observed in older populations. This resistance appears to stem not from a lack of understanding but from deeper emotional and symbolic misalignments between digital tools and the users’ personal values, care routines, or generational identities []. In this context, resistance is not a knowledge deficit but a deliberate, emotionally grounded response to perceived risks, impersonality, or social exclusion. Our synthesis clarifies how such misalignments link to concrete pathways (eg, usability → low self-efficacy → anxiety → avoidance), adding a mechanism to prior critiques.

Reinhardt et al [] claim in their study that resistance to innovation is not merely the opposite of adoption but a distinct phenomenon that operates through its own logic and dynamics, and thus warrants a separate theoretical approach. They proposed the concept of “adoption triggers,” external events or contextual changes that interrupt entrenched resistance and enable eventual uptake. This finding aligns with the results of this review, where participants continued to resist engagement even after usability improvements, suggesting that design enhancements alone are insufficient []. Psychosocial catalysts such as trust in providers, alignment with identity, or significant life transitions may be necessary to shift deeply embedded resistance patterns.

Further support comes from the argument that TAM and UTAUT, widely used models, were not originally developed for health care but rather in organizational contexts. Like IRT, they were formulated outside the health domain and may require adaptation when applied in complex settings, such as digital health for older adults. In their original formulations, these models assume that perceived usefulness and ease of use directly predict technology acceptance. However, in health care, these assumptions are challenged, especially in the context of older adult users []. Health care studies often have to add context-specific variables such as computer anxiety, trust, or physician endorsement to increase explanatory power. This suggests that existing models may benefit from complementary perspectives that foreground resistance shaped by emotional discomfort and identity-related concerns, including symbolic dissonance around how digital health fits with older adults’ roles and expectations. This review affirms the need to view resistance among older adults as socially embedded and identity-relevant, rather than reducible to issues of usability or cognitive evaluation.

Resistance constructs are not intended to replace established acceptance models such as TAM and UTAUT, but to extend them and provide a more complete account of older adults’ technology use and nonuse patterns. Yu et al [], in their research, also extend UTAUT with aging-specific variables such as perceived physical condition, self-actualization needs, and technology anxiety. Their empirical study among Chinese older adults found that while traditional UTAUT predictors (eg, performance and effort expectancy) remain significant, behavioral use was also shaped by perceived physical limitations and psychological needs for self-fulfillment. Notably, the effect of technology anxiety was nonsignificant, suggesting that usability alone does not explain resistance; rather, broader psychosocial and experiential factors must be considered []. These adaptations have introduced constructs such as perceived physical condition, self-actualization needs, and psychosocial well-being to better explain behavioral engagement with health care conversational agents among older adults. Our mapping complements these extensions by locating these constructs within the IRT domains and by indicating which inter-barrier links are explicitly supported by the literature.

Theoretical Implications

This review advances theory on digital health adoption and resistance among older adults in 2 main ways. First, it refines IRT for the context of aging and digital health by highlighting aging-specific resistance themes such as legitimacy gaps, generational digital divides, and anxiety about technology misuse as candidates for further conceptualization and measurement within the original IRT domains. Second, it points to resistance as a dynamic process in which these barriers interact in feedback patterns rather than operating as isolated categories. This mechanism-oriented view complements existing TAMs by underscoring that persistent nonuse reflects active, emotionally and symbolically shaped resistance, rather than merely weak adoption intentions.

Practical Implications

From a gerontechnology and age-inclusive design perspective, the IRT-based model translates the identified barriers and linkages into actionable design and implementation levers to reduce resistance among older adults in primary care. This review has important implications for digital health design, practice, and policy.

First, the disproportionate concentration of extant research within high-income Western countries necessitates a nuanced approach to global implementation, as resistance profiles are not homogenous but are contingent upon divergent socioeconomic structures, varying levels of digital literacy, and culture-specific perceptions of aging []. Addressing these complexities requires a paradigm shift from a reactive model, characterized by a narrow focus on technical troubleshooting and interface simplification, toward a proactive design. While mitigating interface complexity and accommodating sensory impairments remain fundamental requirements, such technical refinements in isolation are insufficient to resolve resistance that is fundamentally anchored in emotional and psychological factors. Consequently, proactive age-tech development should prioritize the alignment of digital interventions with users’ long-standing traditions and the preservation of relational continuity in care []. By acknowledging traditional barriers and framing digital tools as seamless extensions of familiar, trusted care routines rather than disruptive innovations, developers can transition from delivering impersonal technical products to co-creating solutions that resonate with the core identities and values of older populations.

Building on the conceptual model in , breaking the self-reinforcing cycle of resistance requires targeted interventions that address both practical usability barriers and underlying psychological resistance; focusing on interface design or digital literacy alone is unlikely to change deeply rooted patterns of nonuse. Designers need to focus not only on functionality but also on providing emotional reassurance and strengthening the perceived legitimacy and social meaning of digital care. Therefore, solutions should be co-designed with older adults not only to ensure they fit with their routines, communication styles, and cultural values, but also to directly address the specific IRT barriers identified in this review by incorporating strategies that reduce friction and promote confidence. These strategies may include simplifying high-friction tasks by using shorter flows, fewer required fields, larger tap targets, and accessible defaults. Also, designers can provide stepwise guidance and “practice mode,” and offer assisted-digital options such as telephone call-back support, shared on-screen navigation with staff, and on-site digital stations within clinics where staff can help patients complete digital tasks.

Privacy, risk perceptions, and distrust emerged as central barriers in our synthesis. Digital health platforms should incorporate trust-enhancing features, including sustained relationships with known providers, easy access to human support, and clear, simple explanations of data practices. To strengthen perceived legitimacy, systems should preserve care delivery choice (seamless switch to phone or in-person visits), display continuity cues (named clinician, photo, prior encounters), and surface concrete benefits (time saved, refill accuracy, faster appointments). Culturally and linguistically responsive content, combined with feedback that reinforces mastery, can further mitigate anxiety and improve self-efficacy, helping to disrupt the self-reinforcing loops that lead to avoidance. Together, these design-oriented recommendations translate our conceptual findings into practical guidance for technology designers and implementers seeking to reduce resistance among older adults.

Future Research Directions

Future research should investigate the temporal evolution of resistance, including how initial avoidance may shift or diminish over time, and under what conditions. There is a need to explore resistance dynamics among underrepresented populations, such as ethnic minorities, linguistically diverse groups, and individuals living in lower-resource settings. In line with Bevilacqua et al [], emerging work on service-specific acceptance measures for older adults who developed the Robot-Era Inventory as a tailored acceptance scale for a social robotics platform, and called for customizable, context-specific tools tailored to specific technologies and services for older adults, future studies should develop and validate IRT-informed scales tailored to particular digital health modalities []. In addition, longitudinal and mixed-methods designs could provide deeper insight into how resistance is maintained or disrupted. Finally, the development and empirical testing of interventions grounded in IRT would help bridge the gap between theory and design strategies.

Strengths and Limitations

A key strength of this review is its structured, theory-driven synthesis across diverse empirical studies. By applying the IRT to various study designs and health care contexts, this review enhances the conceptual understanding of digital resistance among older adults. It was conducted according to best-practice guidelines for scoping reviews, which reflect established methodological standards.

Several limitations should be noted. First, the search was restricted to English-language publications, which might have excluded relevant studies published in other languages. Second, the review encompasses studies published between 2014 and 2025, a period characterized by rapid technological advancement. Improvements in device usability during this time may have influenced user experiences and patterns of resistance, potentially affecting cross-study comparability. Third, most of the included studies were conducted in high-income Western countries, and the patterns of resistance identified here may not fully capture experiences in lower-income or non-Western contexts, where digital infrastructures, health systems, and cultural norms around aging and technology may differ substantially. This concentration substantially reduces generalizability beyond high-income Western settings and limits the applicability of our findings to global contexts where digital literacy, socioeconomic factors, and cultural perceptions of aging and health care may create distinct resistance profiles. Fourth, none of the included studies reported participants’ cognitive status or used standardized cognitive screening measures. As a result, we could not examine whether resistance barriers vary by cognitive integrity or distinguish attitudinal resistance from barriers related to cognitive impairment, which may influence learnability, confidence, and sustained use of digital health technologies. Finally, the proposed conceptual model has not yet been validated in practice and should be regarded as hypothesis-generating. Future research should operationalize the IRT domains and evaluate their factor structure, reliability, and predictive validity in empirical studies.

Conclusions

Applying IRT to older adults’ experiences with digital health shifts the focus from “lack of readiness” or skills gaps to resistance mechanisms and how technologies are designed and integrated into primary care. Resistance emerges as an active, emotionally rooted process involving functional, psychological, and identity-based barriers to adoption, and this review integrates recurring co-occurrence patterns into a conceptual model, thereby moving beyond prior work that lists barriers in isolation. The synthesis clarifies how usability problems can undermine self-efficacy, increase technology anxiety, and amplify trust and legitimacy concerns, creating feedback loops that reinforce avoidance. Real-world implications: implementation strategies should go beyond technical usability by rebuilding emotional trust, supporting relational continuity, and aligning digital solutions with older adults’ values and routines through meaningful channel choice and transparent communication about risks. In addition, IRT offers a structure for developing domain-specific measures and interventions that address usage, value, risk, tradition, and image barriers, supporting a more realistic and equitable digital transformation in primary care for aging populations.

• Factory-installed solution delivers 10 times the number of performance indicators than standard building automation system connections.
• Advanced analytics enabled by AI help operators optimize ongoing performance.
• Customers using connected chillers experience improved reliability and lower total cost of ownership.

MILWAUKEE, Feb. 2, 2026 /PRNewswire/ — Johnson Controls (NYSE: JCI), the global leader for smart, healthy and sustainable buildings, has launched the next generation of Smart Ready YORK Chillers with factory-installed connectivity to harness real-time performance insights from day one. On average, our customers using connected chillers experience:

• Faster identification of any potential issues remotely, in many cases before they happen.
• 32% fewer unplanned service calls, resulting in greater uptime¹.
• Improved reliability and lower total cost of ownership.

Smart Ready Chillers empower operators to access critical data to set parameters at startup, validate performance baselines and monitor system health using advanced fault detection and diagnostics. This connected solution streamlines commissioning while advanced algorithms immediately begin to learn the chiller to detect even the slightest change in performance, then flag any potential issues as soon as possible to maximize uptime and operating efficiency.

Centrifugal chillers will lead the rollout of this technology with screw and scroll models to follow.

“Smart Ready Chillers mark a significant step forward in our commitment to being our customers’ service partner of choice by connecting all our assets from the start,” said Tyler Smith, vice president, Global Lifecycle Solutions at Johnson Controls. “Johnson Controls pioneered connecting assets in 1883 when Warren Johnson invented the first thermostat and we continue to lead the industry in leveraging connected insights to help our customers operate their buildings more effectively. Smart Ready Chillers help ensure that connectivity can be achieved on day one.”

Flexible Service, Smarter Support

Smart Ready Chillers from Johnson Controls enable digital service capabilities to meet diverse customer needs. Our standard digitally enabled services include the ability to remotely gain insights coupled with on-site support, ideal for all types of customers looking to achieve more reliability, better performance and fewer operational risks.

More Data, Faster Decisions

With over 200 datapoints available immediately – far beyond what building automation systems typically offer – Smart Ready Chillers deliver advanced analytics enabled with AI to help operators catch issues early and optimize ongoing performance. Even small deviations can increase energy costs over a chiller’s lifecycle.

“Chillers are one of the most critical pieces of equipment in a building, and even small performance deviations can jeopardize uptime and drive-up lifecycle costs,” Smith said. “Smart Ready Chillers give operators the real-time data they need to maintain peak efficiency, increase uptime, reduce operating costs and extend the life of their investment, while keeping occupants comfortable.”

Built for Lasting Impact

As a trusted partner, Johnson Controls is committed to helping customers maximize their returns and advance sustainably goals. Smart Ready Chillers deliver more value to help building operators achieve efficiency, reliability and sustainability from the beginning.

Visit www.johnsoncontrols.com/connected-chillers to learn more about how Johnson Controls is delivering innovative chiller solutions to building operators.

MEDIA CONTACT: 

Danielle Canzanella
Direct: +1 414 524 8687
Email: [email protected] 

About Johnson Controls: 

At Johnson Controls (NYSE:JCI), we transform the environments where people live, work, learn and play. As the global leader in smart, healthy and sustainable buildings, our mission is to reimagine the performance of buildings to serve people, places and the planet.

Building on a proud history of 140 years of innovation, we deliver the blueprint of the future for industries such as healthcare, schools, data centers, airports, stadiums, manufacturing and beyond through OpenBlue, our comprehensive digital offering.

Today, Johnson Controls offers the world`s largest portfolio of building technology and software as well as service solutions from some of the most trusted names in the industry.

Visit johnsoncontrols.com for more information and follow @Johnsoncontrols on social platforms. 

¹ Performance results, including reduced unplanned service calls, are based on several factors, including but not limited to system configurations, operating conditions and maintenance routines.  Actual outcomes may vary and are not guaranteed.

SOURCE Johnson Controls International plc

Generative AI is getting really good, really fast. You’ve probably already experienced someone you know, maybe yourself, mistaking a video or image that’s completely AI generated for something that is real. There are some tells, but we’re rapidly approaching a point where AI images, videos, and text will be completely indistinguishable from real life. So. How can we continue to trust what we see online?

To get perspective on this and some possible solutions, GBH’s Morning Edition host Mark Herz spoke with MIT professor and member of the Computer Science and Artificial Intelligence Laboratory, David Karger. What follows is a lightly edited transcript.

Mark Herz: So how good is AI getting at making videos, audio, whatever it is? We always talk about AI slop, but how much of this is getting not-so sloppy?

David Karger: It’s really getting quite good. We’ve been seeing tremendous progress over the past few years and I expect it to continue. There are still some current limitations on AI. You’ve probably noticed that most AI generated videos are pretty short, because AI is still struggling to manage consistency over a very long span of time. But I think we’re going to get there and I think it’s going to be easier and cheaper. You’re gonna be able to start doing this on your own devices instead of relying on big powerful servers in the cloud to do it. I think it’s going to be everywhere and we’re gonna have to deal with that.

Herz: You’ve talked about how it’s unrealistic for social media websites to flag all AI-generated content or to filter it out. And these websites, notably, and under political pressure at times, have abandoned, sometimes dangerously, any institutionalized fact-checking. Are you saying that could be okay somehow?

Karger: I’m not saying that it could be okay. I have been arguing for some time that we can’t really leave that kind of checking in the hands of the platforms because they’re subject to political pressure, as you just mentioned. If the platforms really become the source of fact checking, then whoever is in power is going to be trying to push that fact checking in whatever direction that they want, not just governments, but any organization with an axe to grind is going to try to pressure the platforms. So I think we need to involve more entities, more people, more sources in the fact-checking process. We need to figure out how to ensure that that fact checking can propagate into the platforms, even though the platforms are not doing the fact checking themselves.

Herz: So how do we do that?

Karger: Well, there are a number of groups trying to develop a variety of standards and techniques for labeling content in various ways. There’s something called the Web Consortium Web Credibility Community Group, which is trying to develop some of these standards where you might be able to annotate information in a standardized way with metadata that says that it is real or metadata that say that it’s AI, and you can imagine tools that know how to look for this metadata and make use of it in appropriate ways. For example, I might want to configure my social media to not show me information that has been labeled as AI generated in certain contexts, or, and I actually think that we’re going to have to head more in this direction, I might want to configure my social media to only show me things that have been verified as true or accurate or real by some authority that I trust, but not the platforms.

Herz: But will the platforms let us do that?

Karger: Well, I think we’re gonna have to exert some pressure there. That’s a place for regulation. We’ve seen regulations like the regulation that allows people to download their data from social platforms, which say that, sure, we want to let the platforms operate, but we want to give their users a certain amount of control over their own data and over their experience. I think in similar ways, we want to ensure that users are able to indicate who it is that they trust, which sources. I think news organizations are going to play a very important role here going forward, that they may move from creators of content to more of a fact checking and publishing of what’s true role. I do not think that this is something that can be done with technology. This is all about how we as a society construct knowledge. Most of what each of us thinks we know is something that we have heard from other sources that we trust, whether it be teachers or journalists or governments or whatever. Those aren’t technologies. Those are entities and actors and they are going to continue to be the sources of insight about what is true and what is not. What technology can do is help to transmit that information from the sources somebody trusts to that person through the tools that they’re using.

Herz: So I’m a little confused because we started by talking about how good AI is getting at fooling people. So how are people going to get un-fooled and flag that for other people?

Karger: It’s all about provenance. You need to understand where did this content come from. If I have a particular piece of video that I’m looking at, for starters, I have to believe that it might be real or it might be AI generated. What’s going to differentiate that is somebody who says, “I took this video at this location at this time, and I am trusted by an organization that you rely on so you can trust the video that I took because I am asserting its authenticity.” So the technology is going to support those assertions, and delivering them to people who need them. But the technology’s not going to be able to make that decision for you, “is this accurate or is it not.” The technology is it going to a medium for communicating that information from people who have it to people want it.

Herz: So it sounds like what you’re saying is, it’s gonna be on everybody and not just journalism, although we may have a big part to play, to be highly skeptical. Reminds me when I was in journalism school, they taught us an old saw. They said, “Your mother says she loves you? Check it out.”

Karger: That’s exactly right. I think that we’ve had, for a long time, people talking about critical reading and so on and so forth, being more skeptical of what they see. And I think we need to shift attention from looking at whether the story is internally consistent, which is what you’re often doing with critical reading. That’s not going to survive AI’s ability to create internal consistency. But checking your sources, checking the provenance, where did this come from? Who says that it’s true? That is something that we’re going to have to become much more used to doing. And it’s not enough for us to just develop those habits. We need support from our technology, from our tools. Nobody has the energy or the resources to carefully investigate the sourcing of every single piece of content they encounter on social media. This is where technology can help. Technology is a way to make it easier to do things that we want to do. So if I want to carefully check the sources of every piece of information that crosses my path, well, technological tools can take care of doing that for me under direction.