Category: 3. Business

  • UK/EU Investment Management Update (January 2026) | Insights

    UK/EU Investment Management Update (January 2026) | Insights

    1. UK — Client Categorisation

    2. UK — FCA General Updates

    3. UK — Enforcement

    4. UK — HMT and BoE Updates

    5. UK — EMIR

    6. UK — Cryptoassets

    7. UK — ESG

    8. EU — Capital Markets

    9. EU — RIS

    10. EU — Anti-Money Laundering

    11. EU — EMIR

    12. EU — MiFID II/MiFIR

    13. EU — Securitisation

    14. EU — ESMA General Updates

    15. EU — ESG

    16. EU — Crypotoassets/MiCA

    17. International — Financial Stability Board

    1.  UK — Client Categorisation

    FCA consults on changes to client categorisation and conflicts of interest rules

    On 8 December 2025, the FCA published its long-expected consultation paper (CP25/36), setting out its proposals to amend its rules on professional and retail client categorisation. The FCA’s rules (as derived from Directive 2014/65/EU on Markets in Financial Instruments (MiFID II)) are currently set out in COBS 3 of the FCA Handbook. These proposed changes are expected to encourage firms to offer a greater variety of products to genuine professional clients with higher risk appetites and reduce regulatory complexity given criticisms of the limitations of existing categorisation rules, particularly as regards the “opt up” elective professional client regime.

    CP25/36 includes the following key proposals focusing on the professional client and elective professional client concepts.

    • Removal of the quantitative test. The FCA proposes to remove its existing quantitative test under COBS 3.5.3R(2) and replace it with an enhanced qualitative assessment (as described in the next bullet). This is intended to modernise the elective professional client criteria and prevent consumers from being inappropriately opted out of retail protections while allowing individuals with significant expertise or resources to access the most suitable products. These changes may be particularly welcomed by investors into private funds, for whom the current quantitative test is generally difficult to satisfy. However, we also note that qualitative tests can potentially open firms to risk, especially if a client retroactively complains an incorrect categorisation following subsequent investment losses.
    • Enhanced qualitative assessment. Under the proposed changes, firms will be required to assess clients’ expertise, experience, and knowledge in a robust, holistic manner. Firms will need to take into account a set of relevant factors including clients’ professional experience (focusing more on individuals’ understanding rather than prescriptive employment history within sectors), trading history, financial resilience, and understanding of risk and objectives for opting out of retail client protections. The full proposed list of relevant factors is set out at the draft COBS 3.5.10R in CP25/36. An exception from the qualitative assessment will be introduced for certain wealthy clients (see next bullet).
    • Exception — alternative assessment for wealthy individuals. Under an alternative “wealth-only” assessment, firms will be able to categorise certain individuals as elective professional clients where the individual has investable assets (i.e., a portfolio of designated investments and/or cash) of at least £10 million. As such, the enhanced qualitative test described in the bullet above will not apply to such individuals.
    • Improved safeguards. Clients will be required to actively request to be categorised as an elective professional and give their informed signed consent for the recategorisation after being given sufficient clear information about the protections that they will lose. Firms are prohibited from incentivising, misleading, or pressuring clients to opt out of retail protections.
    • Communications. Firms may proactively engage clients about the option to opt up to professional status only if they have a reasonable basis to believe the client is likely to meet the conditions of being a professional client.
    • Simplification of per se professional client criteria. The FCA proposes to simplify the list of entity types that can be treated as per se professional clients. Under the proposals, any entity authorised or regulated in any country to operate in financial markets can be treated as a per se professional client (including special purpose vehicles controlled by such entities). The FCA notes that this may cause ambiguity so welcomes feedback on whether a list of specific types of per se professional client entity should be retained.

    CP25/36 also covers proposed changes to harmonise various standards on conflicts of interest — however, no material changes to the standards are proposed in this respect.

    The consultation closes on 2 February 2026. The FCA has not provided an indication of when the rules will be finalised.

    Please see also item 9 (EU — Retail Investment Strategy) below to compare the FCA’s approach with what is being proposed in the EU.

    2.  UK — FCA General Updates

    FCA publishes policy statement on final rules for CCIs

    On 8 December 2025, the FCA published a policy statement (PS25/20) setting out final rules for its new CCI regime. As detailed in our January 2025 Update, the CCI regime will replace the UK’s current EU law-derived Packaged Retail Investment and Insurance Products (PRIIPs) regime and disclosure requirements for Undertakings for Collective Investment in Transferable Securities (UCITS).

    Instead of the rigidly prescribed document templates under the PRIIPs and UCITS regimes, the FCA’s new rules set out a minimum set of standardised requirements allowing for more discretion for firms to add information. Under these requirements, firms must produce a consumer-friendly product summary that includes comparable key information about costs, risk and return, and past performance. Distributors must make this summary available to investors pre-sale and highlight key information for consumers and must provide the summary in a durable medium to investors post-sale.

    The PRIIPs regime will be revoked on 6 April 2026, and the CCI regime will come into force on the same date. Firms will then have an 18-month implementation period during which they may begin switching to producing a product summary document and familiarise themselves with the new rules while making the necessary changes to their systems and procedures. Following this, the CCI regime comes fully into force on 8 June 2027.

    Following this update, the Consumer Composite Investment (Designated Activities) (Amendment) Order 2025 (SI 2025/1347) (the CCI Order) was released and laid before Parliament on 18 December 2025, together with an explanatory memorandum. The CCI Order provides that the current treatment of PRIIPS key information documents will be maintained beyond the full FCA transitional period until 8 December 2027 to ensure a smooth transition.

    New iteration of the RIG

    On 11 December 2025, the regulators of the Financial Services Regulatory Initiatives Forum, including the FCA, published the most recent iteration of the RIG. The RIG sets out the current regulatory pipeline for 2026 and provides a useful overview of expected regulatory updates in the next year. In particular, various initiatives are specifically addressed to the investment management sphere including the following:

    • Repealing and replacing AIFMD. The FCA is working on a simplified regulatory framework to replace Directive 2011/61/EU on Alternative Investment Fund Managers (the AIFMD), in a manner tailored to UK markets. HMT published a consultation on the regulation of alternative investment fund (AIF) managers (AIFMs) in April 2025, alongside which the FCA published a Call for Input (as detailed in our May 2025 Update). HMT is now looking to consult on draft legislation alongside the FCA in early 2026.
    • Fund tokenisation. The FCA has consulted on guidance relating to the blueprint tokenisation model and expects to publish its consultation proposals in October 2026 (as further detailed in our November 2025 Update).
    • Liquidity risk management in funds. The FCA is working on implementing international guidance on fund liquidity management, with a consultation on AIFMs intended in H2 2026 (as detailed further below under FCA consults on enhancing fund liquidity management).
    • Data collection review. The FCA is working on transforming its regulatory data model for asset managers and funds to collect accurate and comparable data. This will involve the introduction of new proportional, streamlined regulatory returns following industry consultation.

    We note that this RIG features a 13% reduction in the total number of initiatives since the last edition, suggesting the FCA is committed to its stated intention of streamlining and reducing unnecessary regulation.

    FCA’s guidance to tackle serious non-financial misconduct

    On 12 December 2025, the FCA published guidance to support firms in tackling serious non-financial misconduct (NFM) such as bullying, harassment, and violence. The guidance relates to the new rules on NFM that the FCA published in July 2025 and that come into force from 1 September 2026 (as detailed in our July 2025 Update). The guidance covers how firms can apply the NFM rules on minimum behavioural standards for financial services employees, together with the factors firms should take into account when assessing whether an individual is fit and proper for their role.

    For a discussion of the FCA guidance, see our recent Sidley Update UK Financial Conduct Authority Finalises Non-Financial Misconduct Framework: What Firms Need to Do Now.

    FCA publishes engagement paper on market risk capital requirements

    On 16 December 2025, the FCA published an engagement paper (the Engagement Paper), seeking views on a potential reform of the market risk capital requirements applicable to FCA-authorised MiFID investment firms that deal on own account and operate a trading book. The review focuses on firms’ continued use of market rules derived from the UK version of Regulation (EU) 575/2013 on Capital Requirements (the CRR) under the Investment Firms Prudential Regime (IFPR).

    The FCA notes that while the IFPR sought to move away from bank-centric prudential standards, market risk capital (including the K-NPR requirement on net position risk) continues to reflect assumptions under international standards that may not be proportionate for specialised investment and trading firms. The Engagement Paper therefore explores a range of possible approaches, including targeted amendments to the existing standardised framework, greater reliance on margin-based methodologies, more proportionate internal model approaches, or alternative concepts such as net capital rules or liquidity-based measures. No preferred option is identified at this stage, and firms are required to continue applying existing rules. While no immediate changes arise, the FCA’s approach suggests that future reforms could reduce capital intensity for certain business models.

    The FCA is accepting feedback until 10 February 2026 to inform an upcoming consultation paper in 2026. A new policy is expected in 2027.

    FCA consults on enhancing fund liquidity management

    On 9 December 2025, the FCA published a consultation paper (CP25/38) setting out its proposals on enhancing fund liquidity risk management by authorised fund managers. While the consultation paper mainly focuses on retail investment fund managers, the FCA confirms its plans to consult on liquidity rules under its consultation on proposed reforms to the AIFMD in 2026. The FCA confirmed that such changes will be limited but will include updating liquidity rules for AIFs. For example, the FCA intends to introduce a baseline of liquidity management rules for small AIFMs that are currently not subject to any liquidity management rules. At a minimum, this will involve a requirement of consistency between open-ended funds’ redemption policy and the liquidity of the funds’ portfolio.

    FCA publishes update on the legal challenge against its appointment of a bond consolidated tape provider

    On 3 December 2025, the FCA published a statement confirming that it has reached an agreement to lift a freeze on awarding its bond consolidated tape provider (CTP) contract. Following its application to the High Court to lift the suspension in November 2025, the FCA will be able to sign the contract with Etrading Software (ETS) once ordered by the High Court. The FCA aims to make a decision on ETS’s authorisation ahead of the launch of the tape in June 2026 and will also be able to continue engaging data contributors and users alongside ETS.

    The FCA has stated that the consolidated tape will aim to provide a single and reliable view of the UK bond market and improve the UK’s market transparency and competitiveness. We note from an EU perspective that ESMA selected its first CTP for bonds in July 2025 (as covered in our August 2025 Update).

    3.  UK — Enforcement

    FCA fines Nationwide £44 million for financial crime control failures

    On 12 December 2025, the FCA announced that it has fined Nationwide Building Society (NBS) £44 million for having inadequate anti-financial crime systems and controls in place between October 2016 and July 2021.

    The FCA stated that during this period NBS had inadequate systems for keeping up-to-date due diligence and risk assessments in place for its personal current account users and for monitoring transactions. The FCA also notes that NBS was aware of some customers using their personal accounts for business purposes but did not offer them business accounts, which resulted in NBS not having appropriate processes in place to manage financial crime risks arising from business activity.

    The FCA points out one serious case in particular, where NBS missed opportunities to identify a personal current account customer that used the account to receive fraudulent Covid-19 furlough payments. The customer in question received £27.3 million over the period of 13 months, with approximately £26 million deposited over eight days.

    4.  UK — HMT and BoE Updates

    HMT update on a provisional FCA authorisation regime

    On 4 December 2025, HMT published a statement setting out the government’s intention to introduce a provisional FCA licensing regime for early-stage financial services firms. The introduction of the regime will require primary legislation followed by an FCA consultation so is not expected to be implemented in the near future.

    The provisional regime would aim to reduce barriers to authorisation for financial services firms by enabling the FCA to grant time-limited permissions of up to 18 months. This would allow firms to begin operating their business provisionally in a controlled environment with strong regulatory oversight while working towards obtaining full authorisation under the Financial Services and Markets Act 2000 (FSMA).

    The threshold conditions under the new regime will include having appropriate financial and non-financial resources in place for the period of the provisional licence. The FCA will also require firms to demonstrate that they are able to wind down in an orderly manner at the end of the provisional period if necessary, minimising the risk of disruption and consumer detriment. The FCA’s assessments of applications against the threshold conditions will be proportionate, taking into account, for example, the firm’s stage of development and the fact that the firm is applying for only a time-limited authorisation. During the provisional period, the FCA would also be able to impose restrictions on the amount and type of business a firm can undertake and would have its full range of supervisory/enforcement powers available.

    The regime would not be available for authorised firms seeking a variation of permission, firms seeking permission for activities that are being regulated for the first time, or firms subject to dual regulation.

    HMT consultation on regulatory regimes for benchmarks and benchmark administrators

    On 17 December 2025, HMT published a consultation proposing to repeal the UK version of Regulation (EU) 2016/1011 on Benchmarks (the Benchmarks Regulation) and replace it with a more targeted regime focused only on benchmarks and benchmark administrators that pose systemic risks to UK markets or consumers.

    Under the proposed new Specified Authorised Benchmarks Regime, only benchmarks or administrators designated by HMT (on the FCA’s advice) would be regulated, based on qualitative criteria such as the effect of a benchmark’s cessation or loss of representativeness. Most benchmarks are expected to fall outside scope, with HMT estimating a reduction in regulated benchmarks and administrators of up to 80% to 90%. The consultation also proposes reforms to the regime for overseas benchmarks to preserve access to global benchmarks while simplifying existing access routes.

    For benchmark users, the proposals would remove the requirement to use only benchmarks listed on the FCA’s registers. Firms would be free to use regulated and non-regulated benchmarks, subject to general FCA conduct expectations and appropriate risk management.

    The consultation closes on 11 March 2026. While no immediate changes apply, the proposals signal a shift towards a more flexible, risk-based benchmarks framework, with potential implications for benchmark selection, governance, and fallback planning for investment managers.

    BoE to stress test the private markets ecosystem

    On 4 December 2025, the Bank of England (BoE) launched its second system-wide exploratory scenario exercise, focusing on how private markets would operate under financial stress and mapping the potential effects of stresses on the UK’s financial stability and real economy.

    In particular, the BoE will look into risks concerning opacity, high leverage, weak underwriting standards, and the interconnections between the non-bank ecosystem and the banking system. The exercise will encompass two rounds and include key actors in private markets, including traditional and alternative asset managers, large banks, and institutional investors.

    The BoE’s initiative comes after total assets under management in private market funds have reached approximately US$16 trillion globally, global private equity and private credit assets having increased from US$3 trillion to US$11 trillion over the past decade. The growth of the ecosystem has given rise to an increased interest from government and regulatory bodies in terms of how these markets would behave under stress.

    5.  UK — EMIR

    BoE consultation on exempting post-trade risk reduction transactions from clearing

    On 11 December 2025, the BoE published a consultation concerning the proposal to exempt post-trade risk-reduction (PTRR) transactions from the derivatives clearing obligation under Article 4 of the UK version of the European Market Infrastructure Regulation ((EU) 648/2012) (EMIR). PTRR involves reducing counterparty, operational, and basis risk in derivatives portfolios. Its proposed definition by the BoE is a service provided to two or more counterparties to derivatives transactions for the purpose of reducing non-market risks in derivatives portfolios.

    Generally, over-the-counter (OTC) derivative contracts are required to be cleared by a central counterparty. However, under the Financial Services and Markets Act 2023, the BoE gained the power to exempt transactions relating to PTRR services from the clearing obligation where necessary or expedient for its objective of advancing financial stability. The BoE is now proposing to exercise this power to exempt transactions made under eligible agreements and considers that its proposals would unlock resources by reducing complexity and increasing the efficiency of PTRR services, thereby making PTRR accessible to a wider range of market participants.

    The consultation is open until 11 March 2026.

    6.  UK — Cryptoassets

    HMT publishes Draft Regulations on regulatory regime for cryptoassets

    On 15 December 2025, HMT laid the Financial Services and Markets Act 2000 (Cryptoassets) Regulations 2025 (the Draft Regulations) before Parliament, accompanied by an explanatory memorandum. The Draft Regulations were first published in draft form as the Financial Services and Markets Act 2000 (Regulated Activities and Miscellaneous Provisions) (Cryptoassets) Order 2025 in April 2025 (as detailed in our May 2025 Update).

    The updated Draft Regulations cover “qualifying cryptoassets” which include both qualifying stablecoins and specified investment cryptoassets. In relation to qualifying cryptoassets, certain activities will be classified as specified investments from October 2027, including stablecoin issuance, dealing in cryptoassets as principal or agent, operating a cryptoasset trading platform, arranging deals in cryptoassets, and staking. As such, cryptoasset firms will be regulated by the FCA in the same way as other providers of financial services and will require FSMA authorisation. This is intended to facilitate the detection of suspicious activity and enforcement of sanctions and hold firms accountable more effectively.

    The Draft Regulations relate only to the new regulated activities to be created and their associated consequential amendments. Further provisions on, for instance, market abuse and disclosures will be published separately in due time, following the conclusion of separate FCA consultations (see below item, FCA publishes three consultation papers on cryptoasset regulations). In addition to these changes, consumer protection standards will be amended to match existing standards that apply to other FCA-regulated firms.

    FCA publishes three consultation papers on cryptoasset regulations

    On 16 December 2025, the FCA published three consultation papers setting out its proposed rules and guidance related to the Draft Regulations on the new regulatory regime for cryptoassets. The consultation papers focus on different aspects of the new regime as set out below.

    • CP25/40 sets out proposals for the following cryptoasset activities:

    o Operating cryptoasset trading platforms (CATPs) — The FCA sets out rules on the location, incorporation, and authorisation of operators of UK-authorised CATPs. Firms will be required to have a UK presence and to ensure fair, orderly, and transparent markets. CATP operators would be required to apply non-discriminatory access and trading rules, manage conflicts of interest (including where platforms engage in proprietary trading or admit their own tokens), and implement systems and controls to support market integrity. Additional obligations would apply in respect of transparency, record-keeping, and settlement arrangements.

    o Cryptoasset intermediaries — Cryptoasset intermediaries (including firms dealing as principal or agent or arranging deals) would be subject to conduct standards broadly analogous to those applicable to investment firms, adapted for cryptoasset markets. These include “best execution” style obligations, client order handling rules, and enhanced disclosure requirements.

    o Lending and borrowing — The FCA proposes to regulate cryptoasset lending and borrowing as a form of dealing as principal. Firms offering lending or borrowing services would be required to meet record-keeping requirements, provide clear disclosures to clients, and put in place appropriate arrangements to manage credit, liquidity, and operational risks.

    o Staking — The proposals introduce a regulatory framework for firms providing cryptoasset staking services, which would be subject to requirements aimed at improving transparency for clients, clarifying ownership and control of staked assets, obtaining clients’ consent, and managing operational and governance risks.

    o Decentralised finance (DeFi) — The consultation focuses on identifying where DeFi activities may fall within the regulatory perimeter, particularly where there is a degree of centralised control or identifiable persons exercising influence. The FCA will continue to monitor developments in DeFi and may refine its approach over time as business models evolve and risks become clearer.

    • CP25/41 follows the publication of discussion paper DP24/4 (which we covered in more detail in our January 2025 Update) regarding the implementation and operation of the cryptoassets admissions & disclosures (A&D) regime and the market abuse regime for cryptoassets (MARC).

    o  The A&D proposals outline the information firms must provide to investors. Under the proposals, CATPs would carry responsibility for conducting due diligence on cryptoassets, preventing the admission of qualifying cryptoassets detrimental to investors’ interests, requiring the production and publication of a qualifying cryptoasset disclosure document, and record-keeping.

    o The MARC proposals would introduce prohibitions on insider dealing, unlawful disclosure of inside information, and market manipulation for qualifying cryptoassets admitted to trading. The FCA emphasises that the regime is intended as a pragmatic “day one” market abuse framework that can be recalibrated over time (recognising that cryptoasset markets differ materially from traditional securities markets).

    • CP25/42 sets out the FCA’s proposed prudential framework for firms carrying on newly regulated cryptoasset activities, in the form of an integrated prudential sourcebook, the Core Prudential sourcebook. The consultation seeks to establish baseline own funds and risk assessment requirements, proportionate to the risks posed by different cryptoasset business models, with requirements calibrated by reference to the nature, scale, and complexity of a firm’s activities. Higher standards would apply to firms that hold client assets, operate trading platforms, or engage in activities that could generate systemic risk.

    The deadline for responses to each of the proposals is 12 February 2026, following which the FCA expects to publish respective policy statements in 2026.

    Property (Digital Assets etc) Act receives royal assent

    On 2 December 2025, the Property (Digital Assets etc) Bill received royal assent and came into force as the Property (Digital Assets etc) Act 2025 (the PDAA). The PDAA aims to provide greater certainty and legal protection for those who own and transact with digital assets.

    The PDAA confirms that digital assets can carry property rights despite not falling within one of the two existing legal categories of property (i.e., a ‘chose in possession’ or a ‘chose in action’). As such, the PDAA enables English courts to provide proprietary rights and remedies for owners of digital assets within the framework of English common law. This will be especially relevant to disputes regarding ownership or interference with digital holdings.

    7.  UK — ESG

    FCA publishes proposals outlining regulatory framework for ESG rating providers

    On 1 December 2025, the FCA published consultation paper CP25/34 on its proposals for the regulation of ESG ratings providers. The proposals are designed to improve transparency and trust in ESG ratings following recent legislation that brings ESG ratings under FCA oversight. For further details, see our Sidley Update UK ESG Ratings Providers — Near Final Legislation.

    In designing the new regime, the FCA plans to apply many existing baseline rules that already apply to most other FCA-regulated firms for consistency. In addition, it would introduce tailored rules to address the gaps in existing requirements, focusing on the key areas listed below:

    • Transparency. Tailored rules will include minimum public disclosure requirements for methodologies, data sources, and objectives for users to better understand the rating processes, boosting transparency and clarity.
    • Governance/systems and controls. There will be a focus on ensuring ESG rating providers have robust governance and systems/controls arrangements in place, proportionate to firms’ size and complexity. This includes ensuring that firms retain operational responsibility of the ratings process to ensure oversight and compliance as well as arrangements to safeguard the integrity of the ratings process including quality control, data validation, and methodology reviews.
    • Conflicts of interest. New rules will require firms to take appropriate steps to identify, prevent, manage, and disclose conflicts of interest at both an organisational and personnel level.
    • Stakeholder engagement. Under the new rules, there should be procedures and appropriate avenues in place for stakeholders to provide feedback, correct factual errors, and raise complaints.

    CP25/34 is open until 31 March 2026, and the FCA expects to publish final rules in Q4 2026, with the new regime coming into effect in June 2028.

    8.  EU — Capital Markets

    European Commission adopts legislative proposals on integration of EU capital markets

    On 4 December 2025, the European Commission (the Commission) published a market integration and supervision proposal (the MISP) to other EU institutions regarding its plans to further develop capital market integration and supervision within its savings and investments union (SIU) package. The MISP principally involves a Regulation and a Directive amending various key pieces of EU financial services legislation.

    The MISP is focused on improving EU capital markets’ scale, efficiency, and competitiveness while reducing cross-border compliance complexity and removing barriers that may fragment the SIU (for further background and details, see our May 2025 Update). It also aims to reduce differences in regulatory approaches that have arisen from discretion by EU member states in transposing and interpreting EU Directives and that can unnecessarily complicate cross-border business. As such, the changes seek to:

    • transition certain provisions from Directives to Regulations in order to streamline and simplify regulatory requirements;
    • eliminate the possibility of member states imposing “gold-plating” measures at national level;
    • refine Level 2 provisions;
    • streamline supervisory arrangements that are currently costly, inefficient, or overlapping; and
    • remove general barriers in EU and national frameworks for market operators and investors, including barriers (i) for those operating cross-border who face duplicative or insufficiently harmonised rules, (ii) to passporting, (iii) to interconnection by harmonising rules relating to trading venues, central securities depositories, and asset managers, and (iv) to distributed ledger technology (DLT) innovation and uptake.

    The proposed legislation involves the following:

    o  The amendments are relevant to a wide range of EU Regulations including EMIR, Regulation 600/2014 on Markets in Financial Instruments (MiFIR), Regulation (EU) 2015/2365 on Securities Financing Transactions, Regulation (EU) 2023/1114 on Markets in Crypto-Assets (MiCA), Regulation (EU) 1095/2010 establishing ESMA, and Regulation (EU) 2017/2402 laying down a general framework for securitisation (the Securitisation Regulation), among others.

    o  The MISP Regulation seeks to strengthen ESMA’s role significantly, giving it direct supervisory powers over significant market infrastructures including certain central counterparties (CCPs) and trading venues. ESMA will also be responsible for supervising large asset managers, investment funds, and cryptoasset service providers.

    o The proposals will substantially reform the cross-border distribution regime for UCITS and AIFs (which are currently mainly governed by Directives). The MISP Regulation aims to boost marketing and management of passporting by harmonising marketing rules, reducing host-state discretion, and introducing immediate single market access upon authorisation.

    o The MISP Regulation intends to modernise post-trading rules to facilitate the uptake and use of DLT by expanding and recalibrating the DLT Pilot Regime (including higher thresholds and a simplified regime for smaller operators).

    • Proposal 2025/0382 (the MISP Directive) amends the AIFMD, MiFID II, and Directive 2009/65/EC on UCITS and is intended to complement the MISP Regulation through further harmonisation. In particular, the MISP Directive aims to eliminate national discretions, streamline authorisation/passporting procedures, and enable more efficient group-level operations for EU asset managers.

    o The MISP Directive recognises the concept of an ‘EU asset management group’, aiming to ensure that intra-group arrangements within the EU are no longer treated in the same way as third-party delegation. In effect, UCITS management companies and AIFMs will be able to rely on the resources of other EU group entities without triggering full delegation requirements (provided that the authorised entity remains responsible for oversight). This could reduce operational friction for cross-border groups.

    o ESMA will be mandated to develop regulatory technical standards (RTS) specifying the information, templates, and procedures for authorising UCITS management companies and AIFMs, thereby eliminating divergent national practices. Management passport timelines will also be shortened, and host member states will be prohibited from imposing additional requirements on inbound management companies, in order to reinforce pan-EU freedom to provide services.

    o The proposals introduce an EU-wide depositary passport permitting UCITS and AIFs to appoint as depositaries authorised credit institutions or investment firms with existing EU passports in other member states.

    o The MISP Directive also removes a number of national discretions for UCITS and AIFs, harmonising conduct of business and prudential rules applicable to UCITS management companies and AIFMs.

    o Disclosure obligations are simplified in certain areas, with the obligation for UCITS management companies to produce a standalone key investor information document notably removed.

    o Building on the MISP Regulation, the MISP Directive further strengthens ESMA’s supervisory role. ESMA will be empowered to conduct coordinated annual reviews in cooperation with national competent authorities, to identify and address divergent or duplicative supervisory practices that hinder cross-border operations. ESMA would also have enhanced escalation and intervention powers where national authorities fail to apply EU rules consistently.

    o The proposals align MiFID II with the revised MiFIR framework by transferring most rules governing the authorisation and operation of trading venues from MiFID II to MiFIR.

    • Proposal 2025/0381 proposes to repeal the Settlement Finality Directive (98/26/EC) and replace it with a Regulation and amend the Financial Collateral Arrangements Directive (2002/47/EC). This is aimed at modernising and harmonising protections for cross-border settlement systems and collateral arrangements.

    The MISP has been submitted to the Council of the EU (Council) and European Parliament (EP) for approval, pursuant to the EU’s ordinary legislative procedure. Once finalised, the new rules will represent significant divergence between the UK and EU regulatory frameworks in several respects.

    9.  EU — RIS

    Council and Parliament reach agreement on RIS proposals

    On 18 December 2025, the Council announced that it had reached a provisional political agreement with the EP on an updated RIS. The agreement is subject to approval by both the Council and the EP, prior to going through the formal adoption procedure.

    As discussed in our August 2025 Update, a key proposal under the RIS involves adjusting current EU client categorisation rules by lowering the threshold at which a client can “opt up” from retail to professional client status. The updated framework will allow more retail investors to be treated as professional clients, through individuals fulfilling at least two out of the following three criteria:

    • carrying out either 15 significant transactions over the past three years, 30 transactions over the previous year, or 10 transactions over €30,000 in unlisted companies over the past five years (the existing criterion currently stipulates carrying out 10 transactions per quarter over the previous four quarters);
    • a portfolio size exceeding €250,000 on average over the past three years (currently €500,000 at the moment of the individual’s request); and
    • working and carrying out related activities in the financial sector for at least one year or providing proof of education or training in these activities and an ability to evaluate risk (currently, this criterion requires one year in a professional position in the financial sector).

    See also item 1 (UK — Client Categorisation) above to compare the EU approach with what is being proposed in the UK by the FCA.

    10.  EU — Anti-Money Laundering

    On 4 December 2025, the Commission updated its list of high-risk jurisdictions, which contains jurisdictions that present strategic deficiencies in their national anti-money laundering and counter-terrorist financing regimes. EU entities subject to the anti-money laundering framework are required to apply enhanced measures in transactions involving listed jurisdictions.

    The amendments include both additions (including the British Virgin Islands (BVI)) and removals (including South Africa and Nigeria). Due to the addition of the BVI to the list, from April 2026 (when AIFMD 2.0 becomes law in EU member states), BVI AIFs (and AIFs managed by BVI AIFMs) will not be able to be marketed into the EU (for further details, see our external paper, A U.S. Fund Sponsor’s Perspective on AIFMD 2.0).

    11.  EU — EMIR

    ESMA’s statement on EMIR 3 reporting obligations

    On 11 December 2025, ESMA published a statement on the upcoming reporting obligations that are to be introduced by amendments to EMIR under Regulation (EU) 2024/2987 (EMIR 3) (as discussed in further detail in our December 2024 Update).

    Specifically, ESMA clarifies its approach to the Active Account Requirement (AAR), which entered into force in June 2025 under EMIR 3. The AAR requires EU counterparties to maintain at least one active clearing account with an EU-authorised CCP and use it to clear a representative portion of their trades. The Commission adopted a Delegated Regulation in October 2025 containing RTS specifying conditions relating to the AAR, including reporting requirements. ESMA expects that entities subject to the AAR should make their first reporting submission by July 2026, including any backlog data to demonstrate compliance with the AAR looking back to June 2025.

    In addition, counterparties subject to the new reporting obligation in relation to their clearing obligation at a recognised third-country CCP are expected to report to their supervising authorities annually. ESMA recognises that until the related RTS and implementing technical standards are published, there could be inconsistencies in reporting practices. As such, the first reporting cycle is expected to take place only after the publication of such standards, in 2026.

    12.  EU — MiFID II/MiFIR

    Commission consults on MiFID II research provisions amendments

    On 4 December 2025, the Commission published a consultation on a Delegated Directive amending the MiFID II Delegated Directive (EU) 2017/593 regarding the conditions for the provision of third-party execution and research services to investment firms providing portfolio management or other investment/ancillary services.

    The amendments require firms to inform clients about how firms pay for execution services and research, and they seek to offer firms more flexibility in the way they organise payment for such services. For example, the amendments aim to remove restrictive requirements to bundle payments for research and execution services relating to small and medium-sized enterprises (SMEs), by introducing flexibility on payment methods and removing the requirement to separate payments where it is too cumbersome.

    The consultation closed on 1 January 2026.

    ESMA publishes manual on pre- and post-trade transparency under MiFID II/MiFIR

    On 19 December 2025, ESMA published its updated manual (the Manual) on post-trade transparency under MiFID II and MiFIR. The Manual replaces ESMA’s previous manual on post-trade transparency and follows ESMA’s 2025 review of the RTS under MiFID II and MiFIR RTS. It functions as Level 3 guidance to clarify the practical implementation of obligations relating to post-trade transparency rules and promote common practices.

    Key new sections in the Manual deal with (i) pre-trade transparency for equity instruments and (ii) input/output data reported to or transmitted by CTPs.

    ESMA releases final report on equity transparency requirements for derivatives

    On 15 December 2025, ESMA published its final report on three RTS relating to transparency for derivatives, package orders, and input and output data for the OTC derivatives consolidated tape under amendments to MiFIR made by Regulation (EU) 2024/791 (the MiFIR Review).

    ESMA’s report sets out final draft RTS relating to the following:

    • Transparency requirements for derivatives. These RTS amend Commission Delegated Regulation (EU) 2017/583 (RTS 2) and cover the new transparency and deferral regime for exchange-traded derivatives and OTC derivatives under the MiFIR Review.
    • Treatment of package orders. These RTS set out amendments to the RTS pertaining to the treatment of package orders, reflecting the new scope of derivatives and the liquidity determination.
    • Input and output data. These RTS amend RTS 2 as regards CTPs’ input and output data, reflecting ESMA’s new mandate to develop draft RTS that prescribe data quality requirements for prospective CTPs.

    The final draft RTS are now under the consideration of the Commission to determine whether they are to be adopted.

    13.  EU — Securitisation

    Council and EP adopt negotiation mandate for the securitisation framework

    On 12 December 2025, the Council published the texts of two compromise proposals, following the EP’s publication on 11 December 2025 of draft reports on corresponding topics, each in response to the Commission’s proposals in relation to securitisation:

    • Council Proposal 16740/25 on the Commission’s proposal to amend the Securitisation Regulation as regards creating a specific framework for simple, transparent, and standardised (STS) securitisation (following the EP’s corresponding draft report 2025/0826).
    • Council Proposal 16741/25 on the Commission’s proposal to amend the CRR as regards prudential requirements for credit institutions relating to requirements for securitisation exposures (following the EP’s corresponding draft report 2025/0825).

    The review of the EU framework is being undertaken with the view to amend the prudential and regulatory rules affecting the securitisation market to help the market to reach its potential and contribute to the EU’s competitiveness. In particular, it is intended to help funnel investment into a broader range of real-economy activities, such as SMEs and infrastructure projects.

    Securitisation Regulation

    Both the Council and the EP broadly support recalibrating the EU securitisation framework to revive issuance and investor participation given the contraction of the European securitisation market following the 2008 financial crisis. They agree on introducing a more proportionate and risk-sensitive regime for STS securitisations, including easing due diligence requirements for repeat transactions and streamlining disclosure obligations, particularly for private securitisations.

    The EP also proposes a narrower definition of “public securitisation,” tied primarily to the prospectus regime, and pushes for more meaningful relief for institutional investors, including simplified due diligence for repeat issuances and greater reliance on third-party verification of STS transactions. It also seeks to limit the supervisory burden on competent authorities and soften the sanctioning regime for buy-side due diligence breaches. The Council compromise suggests stronger supervisory oversight at transaction level and a more conservative approach to enforcement and transparency, reflecting member state concerns about systemic risk and supervisory capacity.

    CRR

    Both institutions agree on the need to recalibrate securitisation capital requirements to improve risk sensitivity and address long-standing concerns that EU securitisations are over-penalised relative to their historical performance. There is broad alignment on lowering risk-weight floors for senior tranches and simplifying the framework for recognising significant risk transfer, including replacing rigid mechanical tests with a more principles-based approach.

    The EP proposes deeper reductions and additional caps on risk-weight floors, seeks to avoid differential treatment between originators and investors, and favours earlier and more stable classification of senior tranches to limit capital volatility over the life of a transaction. The Council mandate is more cautious, preserving higher minimum floors, maintaining distinctions between transaction types and investor roles, and embedding greater discretion for supervisors.

    Please refer to our December 2025 Update for the European Central Bank’s opinions on the Commission’s proposed amendments.

    In addition, please see our Sidley Update Proposed Reform of EU Securitization Regulation — Implications for U.S. Securitizations for further background.

    14. EU — ESMA General Updates

    ESMA revises guidelines on liquidity management tools under AIFMD and UCITS

    On 18 December 2025, ESMA published its final report on amended guidelines on liquidity management tools (LMTs) applicable to EU UCITS management companies and AIFMs of open-ended AIFs. The amendments update ESMA’s existing LMT guidelines to reflect changes introduced by the revised AIFMD and UCITS frameworks, with the aim of strengthening supervisory convergence and enhancing fund liquidity risk management across the EU.

    The amended guidelines clarify ESMA’s expectations on the design, calibration, activation, and governance of LMTs, in particular how fund managers should select LMTs in light of their investment strategies. They do not introduce binding new rules but are likely to influence supervisory scrutiny and set a higher benchmark for governance, documentation, and operational readiness. Investment managers may wish to review their liquidity risk frameworks, fund documentation, and internal escalation processes to ensure alignment with ESMA’s clarified expectations, especially where funds are exposed to liquidity stress or market volatility.

    ESMA selects EuroCTP to be the first CTP for shares and exchange-traded funds

    On 19 December 2025, ESMA announced that it has selected EuroCTP as the first CTP for shares and exchange-traded funds in the EU, following a successful assessment under MiFIR. Once authorised, EuroCTP (a Netherlands-based joint venture owned by 15 European exchange groups) will operate the equity CTP for five years under ESMA’s direct supervision. ESMA has described this as a step to enhance post-trade transparency and the attractiveness of EU equity markets.

    15.  EU — ESG

    EU institutions reach provisional agreement on CSRD and CS3D simplification

    On 9 December 2025, the Council and the EP reached a provisional political agreement on the Commission’s Omnibus proposal to reduce the scope of Directive (EU) 2022/2464 on Corporate Sustainability Reporting (CSRD) and Directive (EU) 2024/1760 on Corporate Sustainability Due Diligence (CS3D). For further background, see our Sidley Update EU Omnibus Package: EU Adopts “Stop-the-Clock” Directive and Begins ESRS Simplification Process.

    The provisional agreement covers the following:

    • CSRD. Under the provisional agreement, the scope of the CSRD will be reduced. Listed SMEs and financial holding undertakings will be removed from scope, with the EU company employee threshold raised to 1,000 and a new €450 million net annual turnover threshold being introduced. ‘Wave one’ companies that would be required to comply from FY 2024 can benefit from a transitional exemption if they fall out of scope of the CSRD for 2025 and 2026.
    • CS3D. The threshold requirements for CS3D to apply to EU companies are increased to over 5,000 employees and a net annual turnover of more than €1.5 billion. Additionally, identification of potential and actual adverse impacts in due diligence will be limited to a general scoping exercise, requiring companies to focus on impacts that are the most likely to occur (or that involve direct business partners). Companies should use “reasonably available information” in the process to shift the burden of receiving requests from smaller companies. There will also be no requirement for companies to adopt a transition plan for climate change mitigation and no EU-wide civil liability mechanism, whilst fines will be capped at 3% of global turnover. Transposition has also been postponed to 2028, meaning companies will have to comply with the new measures by July 2029.

    On 16 December 2025, the EP formally adopted its first reading position on the Commission’s proposals. The Council must formally adopt the Directive before it can be published in the Official Journal of the EU.

    ESMA releases research on guidelines on ESG or sustainability-related fund names

    On 17 December 2025, ESMA released its risk analysis research assessing the impact of the fund-naming guidelines on ESG and sustainability-related terms (the Guidelines) that it published in May 2024 (see our June 2024 Update for further details). Overall, ESMA notes that the Guidelines have improved consistency in the use of ESG terms and enhanced investor protection by reducing greenwashing risk.

    ESMA’s research material comprised nearly 1,000 shareholder notifications in reaction to the Guidelines from the 25 largest EU-based asset managers. The study found that 64% of the funds mentioned in the notifications changed their name, most often to avoid using ESG terminology, and 56% updated their investment policies to strengthen their sustainability focus. The research found that funds with higher fossil fuel exposures were more likely to remove ESG terms from their names. ESMA also found that funds with ESG terms in their names reduced their portfolio shares of fossil fuel holdings more than other funds.

    Commission publishes Notice on application of sustainable finance framework and CS3D to defence sector

    On 30 December 2025, the Commission published Commission Notice C/2025/4950 (the Notice) providing guidance on the application of the EU sustainable finance framework to the defence sector. This follows the Commission’s announcement in March 2025 of a plan to boost EU defence capabilities in response to escalating international security threats.

    The Notice is intended to address perceived uncertainty in the market and discourage the blanket exclusion of defence-related investments on sustainability grounds, clarifying that the EU sustainable finance framework neither prohibits investment in the defence sector nor imposes any general financing restrictions. Defence investments, like those in any other sector, must be assessed on a case-by-case basis.

    The Notice covers various parts of the EU sustainable finance framework, including CS3D, CSRD, MiFID II sustainability preferences, Regulation (EU) 2019/2088 on sustainability-related disclosures in financial services (the SFDR), and Regulation (EU) 2020/852 establishing a framework to facilitate sustainable investment (the EU Taxonomy):

    • SFDR. From an SFDR perspective, the Commission highlights that investments in defence companies are not presumed to have adverse sustainability impacts solely due to their sectoral classification. Financial market participants should assess relevant principal adverse impact (PAI) indicators (particularly those relating to human rights compliance and exposure to controversial weapons) by reference to applicable export controls, international humanitarian law, and companies’ internal compliance/due diligence frameworks. For example, as narrowly defined under Commission Delegated Regulation (EU) 2022/1288 supplementing SFDR, the scope of “controversial weapons” is limited to anti-personnel mines, cluster munitions, and chemical and biological weapons.
    • EU Taxonomy. In relation to the EU Taxonomy, the Notice confirms that defence companies may claim alignment for eligible horizontal activities (such as energy efficiency, clean transport, or infrastructure investments), provided that minimum safeguards are met. The absence to date of defence-specific activities from the EU Taxonomy does not negate the sector’s environmental or social sustainability performance.
    • MiFID II. The Commission also clarifies the sector’s interaction with MiFID II sustainability preferences, noting that investment products with exposure to defence companies should not be excluded from distribution to clients with sustainability preferences solely on that basis. Rather, firms must consider minimum safeguards, “do no significant harm” principles, and relevant PAIs.
    • CS3D. The Notice confirms that defence companies fall within the scope of the CS3D on the same basis as other sectors, subject to a targeted exclusion for downstream activities relating to military or dual-use products once export authorisation has been granted. The Commission reiterates that the defence sector may, following careful assessment, contribute to social sustainability objectives including peace and security and should not be treated as a de facto non-sustainable sector under EU sustainable finance rules.

    16.  EU — Crypotoassets/MiCA

    Consortium of major European banks to launch new euro-pegged stablecoin in H2 2026

    On 2 December 2025, a consortium of 10 major European banks announced the formation of a Netherlands-registered company, Qivalis, to launch a stablecoin pegged to the euro in H2 2026. This is intended to counter U.S. dominance in digital payments. Qivalis is distinguished from existing euro-pegged stablecoins by being an institutionally anchored option, unlike its predecessors’ fintech consortium origins. As of the date of this Update, Qivalis is in the process of applying for an Electronic Money Institution licence from the Dutch central bank.

    ESRB Secretariat highlights the challenges digital assets pose to the EU financial system

    On 3 December 2025, the Head of the European Systemic Risk Board (ESRB) Secretariat, Francesco Mazzaferro, gave a speech on the risk that digital assets pose to the competitiveness and integrity of the EU financial system. Drawing attention to an urgent recommendation by the ESRB focusing on third-country multi-issuer stablecoin schemes, he warned that allowing stablecoins issued outside the EU to be freely fungible with those issued in the EU could lead to severe liquidity and systemic-risk problems if many holders were to simultaneously redeem the tokens they hold. Mazzaferro also discussed tokenisation, highlighting its potential to bring efficiency and lower costs through standardised interoperable systems while ensuring that the concentration of multiple market functions into a single intermediary risks creating conflicts of interest and deepening system vulnerability.

    ESMA updates list of MiCA grandfathering periods

    On 1 December 2025, ESMA published an updated list of grandfathering periods for each member state under Article 143 of MiCA. The grandfathering periods allow firms currently compliant with national pre-MiCA cryptoasset regimes to continue providing cryptoasset services pending full MiCA authorisation. Spain was the only Member State to extend its grandfathering period from 12 to 18 months (i.e., until 30 June 2026), significantly mitigating the cliff edge risk potentially affecting firms operating in Spain that were not yet MiCA-compliant at the end of 2025.

    17. International — Financial Stability Board

    FSB report on the growth of the non-bank sector

    On 16 December 2025, the Financial Stability Board (FSB) released its annual report noting that the non-bank sector (including investment funds, trust companies, hedge funds, and money market funds) grew at double the pace of the banking sector, reaching US$256.8 trillion in total global financial assets. In particular, the FSB notes that the section of non-bank financial institutions involved in credit intermediation activities that pose bank-like financial stability risks increased by 12% to US$76.3 trillion, whilst vulnerability metrics (including credit intermediation, maturity/liquidity transformation, and leverage) remained broadly stable.

    However, the report highlights the limitations in data for private credit. As such, the FSB will focus on assessing the potential impact of private credit on financial stability in the year ahead.

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  • TDK establishes TDK AIsight and announces new ultra-low power DSP platform for AI Glasses

    TDK establishes TDK AIsight and announces new ultra-low power DSP platform for AI Glasses

    January 6, 2026

    TDK Corporation (TSE:6762) announces the establishment of a new TDK group company, which will operate as TDK AIsight to address the intersection of physical AI (artificial intelligence) and generative AI, empowering AI glasses with intuitive and compelling user experiences. Building on TDK’s 90 years of innovations and venture spirit, TDK AIsight will focus on the development of custom chips, cameras, and AI algorithms enabling end-to-end system solutions, by combining software technologies such as eye-intent/tracking and multiple TDK technologies such as sensors, batteries, passive components, and other cutting-edge innovations. The name AIsight is derived from the use of artificial intelligence and eyesight.

    The TDK AIsight next-generation SED0112 microprocessor for AI Glasses is the latest part of a planned platform family of Digital Signal Processors (DSPs) integrating a microcontroller, state machine, and hardware Convolutional Neural Networks (CNN) engine. The SED0112’s built-in hardware CNN architecture is specially optimized for eye intent. The microcontroller features ultra-low power DSP processing, eyeGenI™ sensors, and connects to a host processor. SED0112 supports the TDK AIsight eyeGI™ software and algorithms orchestrating the execution of low-power processing, assigning the host processor to be left in a low-power or off state until an event of interest has been detected. The next-gen integrated microprocessor supports a power-saving mechanism, simplifies flow controls, and supports multiple vision sensors at different resolutions. Commercial samples are available now through the TDK AIsight website.

    “TDK AIsight will be a systems solution company building groundbreaking technologies to connect users of AI glasses with generative AI, an innovative type of AI that creates new content and ideas, including conversations, stories, images, videos, and music,” said Te-Won Lee, CEO, TDK AIsight. “We will assemble fully integrated solutions bringing together multiple TDK technologies to seamlessly blend context-aware computing, memory & recall, visual analysis, and scene recognition for compelling user experiences.”

    “TDK AIsight is a bold, concrete embodiment of our strategy for contributing to the AI ecosystem, a core function of our growth with multiple solutions across TDK for both consumer and industrial segments,” stated Noboru Saito, President and CEO of TDK Corporation. “Physical AI represents a strategic domain within TDK’s contribution to the AI ecosystem and enables technologies across devices, systems, and infrastructure to perceive, understand, and interact with the physical world by processing data from a variety of TDK sensors and technologies. This capability leads to autonomous robots, enhanced consumer devices, and intelligent manufacturing that will interact directly with humans and physical processes to sense user context and deliver personalized AI assistance. TDK AIsight will now be part of our portfolio to move this forward.”

    Reach out to TDK-US@publitek.com
    to schedule press and partner meetings with TDK Corporation and its group companies to discuss technology solutions in AI, automotive, ICT, and energy at Booth #15803, Central Hall of the Las Vegas Convention Center at CES 2026, January 6-9, Las Vegas, Nevada, USA. More information on TDK AIsight can be found at www.AIsight.tdk.com ; information about TDK Corporation and its complete technology portfolio can be found at www.tdk.com.

    SED0112 Key Features:

    • Square package 4.6mm x 4.6mm
    • Integrated optimized neural network engine.
    • Camera Support
    • 4 x SES0111 (eye sensor)
    • 1 x SES0113 (contextual sensor)

    SED0112 Applications

    • AI glasses
    • Smart Glasses (AR, Social Media)
    • Industrial Glasses

    SED0112 Glossary

    • AI: artificial intelligence
    • Physical AI: a branch of artificial intelligence that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Generative AI: a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music
    • DSP: Digital Signal Processor
    • CNN: Convolutional Neural Networks

    About TDK Corporation

    TDK Corporation (TSE:6762) is a global technology company and innovation leader in the electronics industry, based in Tokyo, Japan. With the tagline “In Everything, Better” TDK aims to realize a better future across all aspects of life, industry, and society. For over 90 years, TDK has shaped the world from within; from the pioneering ferrite cores to cassette tapes that defined an era, to powering the digital age with advanced components, sensors, and batteries, leading the way towards a more sustainable future. United by TDK Venture Spirit, a start-up mentality built on visions, courage and mutual trust, TDK’s passionate team members around the globe pursue better—for ourselves, customers, partners, and the world. Today, the state-of-the-art technologies of TDK are in everything, from industrial applications, energy systems, electric vehicles, to smartphones and gaming, at the core of modern life. TDK’s comprehensive, innovative-driven portfolio includes cutting-edge passive components, sensors and sensor systems, power supplies, lithium-ion and solid state batteries, magnetic heads, AI and enterprise software solutions, and more—featuring numerous market leading products. These are marketed under the product brands TDK, EPCOS, InvenSense, Micronas, Tronics, TDK-Lambda, TDK SensEI, and ATL. Positioning the AI ecosystem as a key strategic area, TDK leverages its global network across the automotive, information and communication technology, and industrial equipment sectors to expand its business in a wide range of fields. In fiscal 2025, TDK posted total sales of USD 14.4 billion and employed about 105,000 people worldwide.

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  • Louisiana Wins Back-to-Back Platinum Deal of the Year, Signaling a New Era of National Competitiveness – Entergy

    1. Louisiana Wins Back-to-Back Platinum Deal of the Year, Signaling a New Era of National Competitiveness  Entergy
    2. Hyundai Steel’s $5.8 billion project wins Business Facilities’ Platinum Deal of the Year  Gonzales Weekly Citizen
    3. Business Facilities Announces 2025 Deals Of The Year & 2025 Impact Awards  Business Facilities
    4. Editorial: State in good position to address challenges in 2026  NOLA.com
    5. Hyundai Steel’s $5.8B steel mill in Ascension is named Deal of the Year  Baton Rouge Business Report

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  • Blackstone Announces Fourth-Quarter and Full-Year 2025 Investor Call

    Blackstone Announces Fourth-Quarter and Full-Year 2025 Investor Call

    NEW YORK – January 6, 2026 – Blackstone (NYSE:BX) announced today that it will host its fourth-quarter and full-year 2025 investor conference call via public webcast on January 29, 2026 at 9:00 a.m. ET.
     
    To register, please use the following link: https://event.webcasts.com/starthere.jsp?ei=1748258&tp_key=b779ba06d9
     
    For those unable to listen to the live broadcast, there will be a webcast replay on the Shareholders section of Blackstone’s website at https://ir.blackstone.com/.
     
    The audio replay will also be available on our podcast channels, including Spotify, Apple Podcasts and SoundCloud, approximately 24 hours after the event.
     
    Blackstone distributes its earnings releases via its website, email lists and Twitter account. Those interested in firm updates can sign up here to receive Blackstone press releases via email or follow the company on X (Twitter) @Blackstone.
     
    About Blackstone
    Blackstone is the world’s largest alternative asset manager. We seek to deliver compelling returns for institutional and individual investors by strengthening the companies in which we invest. Our over $1.2 trillion in assets under management include global investment strategies focused on real estate, private equity, credit, infrastructure, life sciences, growth equity, secondaries and hedge funds. Further information is available at www.blackstone.com. Follow @blackstone on LinkedIn, X (Twitter), and Instagram. 
     
    Contact
    Blackstone Public Affairs
    New York
    +1 (212) 583-5263


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  • The Daily — National tourism indicators, third quarter 2025

    The Daily — National tourism indicators, third quarter 2025



    Released: 2026-01-06

    Tourism gross domestic product (GDP), in real terms, grew 0.9% in the third quarter of 2025, matching the pace set in the second quarter. By comparison, economy-wide real GDP by industry was up 0.5% in the third quarter, following a 0.2% decline in the second quarter. Tourism GDP accounted for 1.70% of nominal GDP in the third quarter, nearly unchanged from the second quarter (1.71%).

    Chart 1 

    Chart 1: Tourism and major industrial sectors, gross domestic product, third quarter of 2025

    Tourism and major industrial sectors, gross domestic product, third quarter of 2025


    Chart 1: Tourism and major industrial sectors, gross domestic product, third quarter of 2025

    Tourism gross domestic product increases in all industry groupings

    In the third quarter, growth in tourism GDP was mainly driven by increased activity in the accommodation (+1.2%) and transportation (+1.2%) industries. Food and beverage services (+0.5%), other tourism industries (+0.4%), and non-tourism industries (+0.8%) were also up in the quarter.

    Total tourism spending rises

    Total tourism spending was up 0.7% in the third quarter, compared with a 0.9% increase in the second quarter. Higher domestic (+0.5%) and international (+1.2%) tourism spending both contributed to the overall growth in the third quarter.

    Accommodation services (+1.4%) was the largest contributor to growth in the third quarter, which was tempered by lower passenger air transport (-1.0%).

    Tourism spending by international visitors is up across all tourism products

    Tourism spending by international visitors was up in the third quarter (+1.2%), following a 5.6% decline in the previous quarter. Most tourism products registered gains in the third quarter, led mainly by vehicle rental (+5.1%), vehicle repair (+2.6%), and travel services (+2.6%). Meanwhile, accommodation increased 1.0% in the third quarter, after falling 6.4% in the second quarter.

    International visitors’ share of tourism spending in Canada (22.3%) was virtually unchanged in the third quarter, after falling to its lowest level in over two years in the second quarter. As Canada-US trade tensions continued in the second quarter, fewer Americans travelled to Canada, while Canadian tourists travelled less to the United States and spent more in Canada.

    Chart 2 

    Chart 2: Share of tourism spending in Canada by international visitors

    Share of tourism spending in Canada by international visitors


    Chart 2: Share of tourism spending in Canada by international visitors

    Tourism spending in Canada by Canadian residents slows in the third quarter

    Domestic tourism spending by Canadian residents was up 0.5% in the third quarter compared with a 2.9% increase in the second quarter. Gains in the third quarter were posted in accommodation services (+1.6%), pre-trip expenditure (+4.4%), and vehicle rental (+11.3%). The Canada Strong pass, valid from June 20 to September 2, likely contributed to a 3.9% increase in passenger rail transport.

    Spending on passenger air transport decreased 1.6% in the third quarter, coinciding with lingering Canada-US trade tensions and cancelled flights due to the flight attendants’ strike in August.

    According to the Canadian Survey of Consumer Expectations for the third quarter, 33.6% of Canadians planned on spending more while vacationing in Canada, and 53.1% of Canadians planned on spending less while vacationing in the United States.

    Tourism jobs increase in the third quarter

    The number of jobs attributable to tourism increased 0.6% in the third quarter of 2025, the same pace as in the second quarter. By comparison, the economy-wide number of jobs was down 0.3% in the third quarter.

    All industries posted gains in the third quarter except for recreation and entertainment (0.0%), with food and beverage (+0.5%), accommodation (+0.7%) and non-tourism (+0.7%) industries contributing the most to the growth.

    Tourism’s share of economy-wide jobs was 3.36% in the third quarter, slightly up from the second quarter (3.33%).

    Chart 3 

    Chart 3: Tourism spending, tourism gross domestic product (GDP) and jobs attributable to tourism

    Tourism spending, tourism gross domestic product (GDP) and jobs attributable to tourism


    Chart 3: Tourism spending, tourism gross domestic product (GDP) and jobs attributable to tourism

    Looking ahead

    The number of Canadian travellers returning to Canada by land and by air posted declines in both October and November 2025 compared to the same months of the previous year, according to leading indicators of Frontier Counts data.

    Driven by growth in air travel, the number of non-resident travellers arriving in Canada increased in October. However, the number of non-resident travellers arriving in Canada by air and by land decreased in November.




    Sustainable development goals

    On January 1, 2016, the world officially began implementing the 2030 Agenda for Sustainable Development—the United Nations’ transformative plan of action that addresses urgent global challenges over the next 15 years. The plan is based on 17 specific sustainable development goals.

    The national tourism indicators are an example of how Statistics Canada supports the reporting on the global goals for sustainable development. This release will be used in helping to measure the following goal:

      Note to readers

    With the third quarter 2025 release of the national tourism indicators, data for the first and second quarters of 2025 have been revised.

    Gross domestic product (GDP) expressed in real and nominal terms is at basic prices, unless otherwise specified.

    In this article, tourism GDP refers to the price-adjusted or real measure of GDP, unless otherwise stated.

    Growth rates for tourism spending and GDP are expressed in real terms (that is, adjusted for price changes), using reference year 2017, as well as adjusted for seasonal variations, unless otherwise indicated.

    Tourism jobs data are also seasonally adjusted. Tourism’s share of economy-wide jobs is calculated using seasonally adjusted values.

    Tourism’s share of economy-wide GDP is calculated from seasonally adjusted nominal values.

    Economy-wide GDP is obtained from table 36-10-0449-01. Economy-wide total number of jobs is obtained from table 36-10-0207-01.

    For information on seasonal adjustment, see Seasonally adjusted data – Frequently asked questions.

    Associated percentage changes are presented at quarterly rates unless otherwise noted.

    Non-tourism industries, also referred to as other industries, are industries that would continue to exist in the absence of tourism. For example, retail trade industries, which benefit from tourism activity, would not cease to exist in the absence of tourism. Tourism GDP takes into account the goods and services produced by these other industries and purchased by tourists.

    Non-tourism products, also referred to as other products, are products for which a significant part of its total demand in Canada does not come from visitors, such as groceries, clothing and alcoholic beverages bought in stores.

    The national tourism indicators are funded by Destination Canada.

    The Contribution of Tourism in 2024: Jobs and Economic Growth Across Canada, an infographic by Destination Canada and Statistics Canada, summarizes the wide range of economic and societal contributions of the tourism sector.

    Next release

    Data on the national tourism indicators for the fourth quarter of 2025 will be released on March 27, 2026.


    Products

    The Economic accounts statistics portal, accessible from the Subjects module of the Statistics Canada website, features an up-to-date portrait of national and provincial economies and their structure.

    The Latest Developments in the Canadian Economic Accounts (Catalogue number13-605-X) is available.

    The User Guide: Canadian System of Macroeconomic Accounts (Catalogue number13-606-G) is available.

    The Methodological Guide: Canadian System of Macroeconomic Accounts (Catalogue number13-607-X) is available.

    Contact information

    For more information, or to enquire about the concepts, methods or data quality of this release, contact us (toll-free 1-800-263-1136; 514-283-8300; infostats@statcan.gc.ca) or Media Relations (statcan.mediahotline-ligneinfomedias.statcan@statcan.gc.ca).

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  • Journal of Medical Internet Research

    Journal of Medical Internet Research

    The integration of the Internet of Things (IoT) into health care has revolutionized the industry by introducing a new paradigm of connectivity and data exchange, driven by rapid advancements in IoT, artificial intelligence, and machine learning [-]. This era, known as Healthcare 4.0, can be leveraged to enhance acute disease care, manage chronic diseases, and support self-health management []. The COVID-19 pandemic accelerated the adoption of user-friendly IoT devices [-], with wearable medical devices emerging as key allies by offering real-time health monitoring, continuous data transmission, and advanced remote health management [-].

    Globally, the integration of IoT in health care has enhanced efficiency, improved patient care, and generated significant economic value [,]. By 2029, the global IoT health care market volume is projected to reach US $134.40 billion []. This indicates strong, sustained growth driven by the increasing adoption of IoT technologies in health care around the globe [,]. For instance, China has made significant progress in integrating health information technologies into the health care system, driven by initiatives such as “Internet Plus Health Care” and the “Healthy China 2030” plan [-]. The United States leads in IoT and intelligent health care system development, supported by substantial investments and a robust ecosystem of startups and tech companies driving advancements in artificial intelligence and IoT [-]. Europe also shows considerable progress, emphasizing regulatory frameworks, standardization, and interoperability to foster innovation and data protection [,].

    While IoT holds considerable promise to transform health care by reducing costs and improving access, understanding the factors influencing its adoption requires more focused research []. Although literature reviews with a quantitative approach have examined technology adoption in health care, existing meta-analyses that include technologies with IoT features remain fragmented, as they have largely focused on broader or adjacent technological domains and have typically emphasized a specific adoption model. For instance, meta-analyses on mobile health have focused on the Unified Theory of Acceptance and Use of Technology (UTAUT) [] and the Technology Acceptance Model (TAM) []. Meta-analyses on eHealth have predominantly focused on the TAM [] and continuance intention []. Meta-analyses specific to smart wearable health care devices have examined attitude and intention using UTAUT and TAM [], as well as the effects of perceived usefulness and perceived ease of use on intention, with a focus on Hofstede’s cultural dimensions as moderators []. Taken together, these studies often treat health care technology adoption in general terms and do not account for the unique characteristics of IoT.

    Our study addresses this gap by providing a comprehensive meta-analysis and a weight analysis specifically focused on IoT adoption in health care. We synthesize findings from primarily quantitative articles on the adoption of IoT in health care, particularly on interconnected devices that monitor and transmit real-time health care data, enabling smarter solutions [], such as smart sensors, remote monitoring devices, and health-focused IoT platforms. Our meta-analytic approach integrates findings from different theoretical perspectives, including technology adoption models such as the TAM and UTAUT and health-specific models such as the Health Belief Model (HBM), allowing for a more holistic understanding of adoption dynamics in health care contexts. Moreover, our dual-method approach, combining meta-analysis with weight analysis, identifies the strongest and most reliable predictors of adoption and maps the theoretical foundations most frequently and effectively used in this field. This analysis goes beyond prior reviews, offering new evidence-based insights to guide health care technology developers, practitioners, and researchers. The objectives of this study are, first, to identify key predictors of IoT health care adoption through a comprehensive meta-analysis and weight analysis, and second, to determine the most influential and empirically supported theories used to explain IoT adoption in health care settings.

    Overview

    We performed a meta-analysis to examine the factors influencing the adoption of IoT technologies in health care, synthesizing findings from a range of quantitative studies. By focusing on primary quantitative research articles, we aimed to identify the most significant predictors and the theoretical models most commonly used to explain IoT adoption in health care settings.

    Information Sources and Search Strategy

    This meta-analysis followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines []. The literature search was conducted using a keyword-based search across the Web of Science and PubMed databases to identify studies examining IoT adoption in health care. The search strategy incorporated title, abstract, and keyword searches, using Boolean operators (AND and OR) and database-specific filters. The keywords used in our search were related to IoT technology, relevant variables, quantitative methods, and exclusion criteria (). We included records published up to the end of 2024. The complete search strategy, including search terms and Boolean logic, is provided in .

    Table 1. Map for the keyword search in online databases.
    Relevant terms Relevant variables and theories Methodologies Exclusion of irrelevant topics
    Internet of Things Intention to adopt Structural equation Systematic
    IoT Behavioral intention Structural equation modeling Literature review
    Smart Acceptance Partial least squares structural equation modeling Postadoption
    Intelligent Adopt Partial least Meditation
    Health care wearable device Adoption Path analysis Contact tracing app
    Medical wearable technology Using Regression Fitness app
    Health management Use N/Aa Electronic health record
    Health measurement Usage N/A Telemedicine
    N/A Intention to use N/A Mindfulness app
    N/A Unified Theory of Acceptance and Use of Technology 2 N/A N/A
    N/A Unified Theory of Acceptance and Use of Technology N/A N/A
    N/A Technology Acceptance Model N/A N/A

    aN/A: not applicable.

    Selection Criteria

    Initial screening was conducted using database filters. In the second screening, 2 (IV and MNZ) independent reviewers assessed the titles and abstracts for relevance, resolving disagreements through discussion or arbitration by a third reviewer. The full-text screening followed the same procedure. The reports assessed for eligibility were exported to an Excel (Microsoft Corporation) file, and all included papers were imported into Zotero (Corporation for Digital Scholarship), which is a reference management software. When a paper was unavailable, the authors were contacted.

    Inclusion criteria comprised peer-reviewed articles with a quantitative approach to health care technology adoption written in English. Reasons for exclusion included not reporting the significance of the relationships between variables; the technology lacking IoT features or being unrelated to health care; the target variables being unrelated to adoption or focusing solely on postadoption behaviors; and studies lacking empirical data or reporting qualitative results only. The workflow and search conditions are depicted in more detail in the “Results” section.

    Data Extraction

    A standardized data extraction form was developed before data extraction. Data extraction was performed in Excel, and for each article, we detailed the study characteristics, methodology, type of technology, and the effects measured across multiple paths. The extracted aspects and their descriptions are provided in Table S1 in . We assessed paper quality by examining the publishing journal metrics, the methods employed, sample size, and the scales used to measure each construct. The standardized β coefficients were extracted as the primary effect measure. As some authors used different names to represent the same variable, several variables had to be merged to conduct our analysis. This process was carried out by reading each variable definition and identifying the items used to measure them. Examples of variable mergers are provided in Table S2 in , and the individual studies included in the analysis are detailed in Table S3 in .

    Descriptive Analysis

    We extracted metadata from each study to perform a descriptive analysis of publication trends, journal quality, and research domains. Data on publication year were used to assess the chronological distribution of studies. To evaluate journal quality, we matched each journal with its SCImago Journal & Country Rank classification and categorized them into quartiles (Q1-Q4). The disciplinary scope of the journals was identified based on their SCImago subject area classifications. We also recorded the journal title and publication frequency to identify the journals that published the most research. Country-level data were extracted based on the origin of the study sample or study location. We computed the number of studies and total sample sizes per country to identify regions with the highest research activity. To understand the theoretical foundations employed across studies, we reviewed each article’s methodology and coded the theories used to model technology adoption behavior. We also recorded whether these models were used independently or in combination (eg, UTAUT extended with Protection Motivation Theory [PMT] constructs).

    Weight Analysis

    The weight analysis was conducted to uncover the predictive power of independent variables []. This weight provides a measure of the relative importance or consistency of statistical significance for each variable across multiple analyses. For the weight-analytic approach, we focused on the influence of each independent variable on several dependent variables and limited our analysis to relationships investigated 3 or more times [,]. The weight (Wi) of an independent variable i is calculated as the ratio of the number of times it was found to be statistically significant (Si) to the total number of times it was examined (Ei), as expressed in the following equation:

    Wi = Si/Ei

    Meta-Analysis

    Meta-analyses allow us to quantitatively compare effect sizes across relationships between constructs using suitable metrics to capture these effect sizes, including standardized regression coefficients [,]. This analysis followed best practices outlined previously [-]. In our study, the necessary inputs for performing the meta-analysis were the standardized regression coefficients (β) and the sample sizes for each relationship examined 3 or more times across studies. Following the approach of Peterson and Brown [], β values were transformed into approximate correlation coefficients as r=β+0.05, where λ=1 []. All correlation coefficients were Fisher z-transformed to stabilize variance, and SEs were computed.

    A random-effects model was used to account for both within- and between-study variance, justified by the heterogeneity in study populations, methods, and contexts. Random-effects weights were calculated using the DerSimonian and Laird model, and weighted mean effect sizes were computed using inverse-variance weights, which use tau-squared (τ2) [,]. Heterogeneity was assessed using the Q statistic and the I2 index []. We also calculated the lower and upper bounds of the 95% CIs, z scores, and 2-tailed P values to assess statistical significance and interpret the magnitude of the observed effects. Final pooled effect sizes and CIs were then back-transformed from Fisher z to the correlation coefficient metric (r). All calculations were performed manually in Excel.

    Publication Bias Analysis

    The Egger test was used to statistically examine the presence of publication bias by regressing the standard normal deviate on precision []. The analysis was performed using Excel’s data analysis regression tool, which applies standard ordinary least squares regression, and a significant intercept (P<.10) was interpreted as evidence of asymmetry and possible publication bias. A funnel plot was constructed to visually assess publication bias using the tool Meta-Essentials []. The trim-and-fill method was used to estimate the number and influence of missing studies. Heterogeneity for the included studies was assessed using the I2 statistic, where a value over 75% is interpreted as substantial heterogeneity, using the following formula, where k is the number of studies and Q the Cochran Q statistic:

    I2 = max(0; {Q – [k – 1]}/Q) × 100%

    To evaluate regional bias, we conducted a subgroup analysis with 2 groups: one comprising studies conducted in China and the other comprising studies conducted in the remaining countries. For each group, we calculated the combined effect sizes, SEs, CI lower and upper limits, and the I2 statistic.

    Combining Weight and Meta-Analysis Results: The Most Used Adoption Models

    To synthesize the relative strength of relationships across studies and adoption models, we combined the results from the weight analysis and meta-analysis. The weight analysis assessed the consistency and prominence of specific predictors by calculating the proportion of studies that reported statistically significant relationships for each path, referred to as the weight. In parallel, the meta-analysis provided pooled average effect sizes and significance levels across studies using a random-effects model. This dual approach offers a more comprehensive understanding of which constructs consistently predict behavioral intention or usage in the context of IoT adoption in health care and enables an evidence-based comparison of theoretical frameworks based on empirical support.

    We then visually mapped the structure of each adoption model, such as the TAM and the HBM, using conceptual diagrams. In these figures, each arrow represents a theoretical path, and its thickness reflects the weight. Thicker lines indicate a weight above 0.700, representing paths supported by a high proportion of studies. The numerical values attached to each path represent the average effect size based on the random-effects meta-analysis, along with the corresponding P value. This dual representation enables a clearer comparison between the predictive strength (effect size) and consistency (weight) of each construct within and across models.

    Descriptive Analysis

    Papers on IoT health care adoption show an increasing trend, with 89 of the 109 (81.7%) studies in our analysis published between 2020 and 2025, and the earliest published in 2011. According to the SCImago Journal & Country Rank, most papers appeared in Q1 journals (n=63, 57.8%), followed by Q2 (n=36, 33%) and Q3 (n=10, 9.2%), with no papers published in Q4. These studies span major research areas related to health and medicine, information systems, and computer science. In total, 75 unique journals were represented, with PLoS One (n=6), Frontiers in Public Health (n=5), Technological Forecasting and Social Change (n=5), and International Journal of Environmental Research and Public Health (n=4) being the most frequently appearing journals (see Table S3 in ).

    In our analysis of 109 studies (see ), we identified 115 unique datasets totaling 46,508 individuals (see Table S1 in the ). Studies conducted in China, South Korea, and the United States accounted for a large portion of the total sample and represented the greatest number of publications (see ).

    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart.
    Table 2. Number of papers and total sample size per country.
    Country Dataset count (N=115) Sample size (N=46,508)
    China 39 17,068
    South Korea 12 6406
    The United States 9 5445
    India 8 2924
    Taiwan 8 1896
    The Kingdom of Saudi Arabia 5 1798
    Bangladesh 2 1213
    Pakistan 3 1194
    Turkey 4 1040
    Ghana 2 965
    Multiple countries 4 1312
    Indonesia 1 772
    Malaysia 2 628
    France 3 515
    Iraq 1 465
    Oman 2 442
    Romania 1 440
    The United Arab Emirates 2 431
    Switzerland 2 323
    Singapore 1 306
    Nepal 1 280
    Japan 1 233
    Italy 1 212
    Jordan 1 200

    Considering the theories addressed in each paper, the TAM and the UTAUT have been extensively examined compared with other theories (see ). These models serve as the theoretical foundation for 92 of the 109 (84.4%) papers included in our study. Other theories, such as the HBM, PMT, Task-Technology Fit (TTF) Theory, Privacy Calculus Theory, Diffusion of Innovation Theory, and Theory of Planned Behavior, have also been addressed—sometimes as the primary theoretical foundation and other times to extend the TAM or UTAUT.

    Figure 2. Theoretical foundation of the papers included in our analysis. DOI: Diffusion of Innovation; HBM: Health Belief Model; PCT: Privacy Calculus Theory; PMT: Protection Motivation Theory; TAM: Technology Acceptance Model; TPB: Theory of Planned Behavior; TTF: Task-Technology Fit; UTAUT: Unified Theory of Acceptance and Use of Technology.

    Weight Analysis

    A weight analysis examines the strength of the relationship between an independent and a dependent variable. The weights of the identified relationships are analyzed and presented in . The significance of a relationship’s weight is calculated by dividing the number of instances in which the relationship is statistically significant by the total number of studies that investigated it. A weight of 1 indicates that the relationship is significant in all examined studies, whereas a weight of 0 indicates that it is not significant in any of the studies.

    Table 3. Identified paths with the nonsignificant paths, the significant relationships, the total paths, and the respective weights.
    Dependent and independent variables Significant Nonsignificant Total Weight=significant/total
    Attitude
    Effort expectancy 19 4 23 0.826
    Barriers 4 2 6 0.667
    Benefits 3 0 3 1
    Facilitating conditions 4 0 4 1
    Performance expectancy 20 2 22 0.909
    Privacy and security 2 1 3 0.667
    Social influence 6 1 7 0.857
    Behavioral intention
    Aesthetic appeal 4 0 4 1
    Attitude 27 1 28 0.964
    Barriers 7 6 13 0.538
    Benefits 6 0 6 1
    Compatibility 4 3 7 0.571
    Effort expectancy 36 23 59 0.61
    Ethics 2 1 3 0.667
    Facilitating conditions 20 8 28 0.714
    Financial cost 12 9 21 0.571
    Functional congruence 4 1 5 0.8
    Habit 7 0 7 1
    Health 3 1 4 0.75
    Health consciousness 7 4 11 0.636
    Hedonic motivation 10 5 15 0.667
    Image 4 2 6 0.667
    Innovativeness 5 3 8 0.625
    Perceived severity 2 3 5 0.4
    Perceived vulnerability 3 5 8 0.375
    Performance expectancy 68 10 78 0.872
    Privacy and security 13 13 26 0.5
    Reliability 5 0 5 1
    Self-efficacy 10 1 11 0.909
    Social influence 31 10 41 0.756
    Technology anxiety 2 4 6 0.333
    Trust 9 3 12 0.75
    Actual behavior
    Behavioral intention 16 1 17 0.941
    Effort expectancy 2 1 3 0.667
    Facilitating conditions 3 0 3 1
    Health consciousness 2 2 4 0.5
    Innovativeness 2 1 3 0.667
    Perceived vulnerability 2 1 3 0.667
    Performance expectancy 3 1 4 0.75
    Social influence 3 0 3 1
    Performance expectancy
    Barriers 2 3 5 0.4
    Compatibility 6 2 8 0.75
    Convenience 3 0 3 1
    Effort expectancy 27 4 31 0.871
    Facilitating conditions 2 2 4 0.5
    Health consciousness 6 2 8 0.75
    Image 3 4 7 0.429
    Innovativeness 4 0 4 1
    Privacy and security 4 2 6 0.667
    Reliability 8 3 11 0.727
    Self-efficacy 7 0 7 1
    Social influence 8 3 11 0.727
    Trialability 2 1 3 0.667
    Trust 3 2 5 0.6
    Task-technology fit 5 0 5 1
    Effort expectancy
    Compatibility 7 0 7 1
    Facilitating conditions 6 0 6 1
    Image 2 2 4 0.5
    Innovativeness 7 0 7 1
    Privacy and security 2 2 4 0.5
    Reliability 4 0 4 1
    Self-efficacy 7 0 7 1
    Social influence 3 1 4 0.75
    Trialability 2 1 3 0.667
    Task-technology fit 3 0 3 1
    Task-technology fit
    Task characteristics 3 1 4 0.75
    Technology characteristics 4 0 4 1

    In the context of technology adoption at the individual level, independent variables are considered “well-utilized” if they have been tested at least 5 times. Variables tested fewer than 5 times but with a weight of 1 are regarded as “promising” predictors []. To be classified as a “best” predictor, an independent variable must have a weight of 0.800 or higher and must have been examined at least 5 times [].

    In our research, we analyzed the impact of several independent variables on the dependent variables attitude, behavioral intention, actual use, performance expectancy, effort expectancy, and TTF. For the weight analysis, we included relationships that were examined 3 or more times, resulting in 67 relationships and 31 unique predictors that met this criterion. The most studied target variable was behavioral intention, with 25 predictors.

    In our research, the relationships considered the “best” predictors for attitude are effort expectancy, performance expectancy, and social influence, as each has more than 5 identified relationships and a weight greater than 0.800. For behavioral intention, the best predictors are attitude, performance expectancy, habit, self-efficacy, functional congruence, reliability, and benefits. Aesthetic appeal, with a perfect weight of 1, is considered a promising predictor of intention due to the limited number of studies. Social influence, facilitating conditions, and trust, although not classified as the best predictors, remain important because their weights exceed 0.700 and are supported by a substantial number of studies. It is also noteworthy that privacy and security, barriers, vulnerability, severity, compatibility, and financial cost yielded more inconsistent results, with many studies reporting statistically nonsignificant findings.

    For actual behavior, behavioral intention is the best predictor, while facilitating conditions and social influence are considered promising predictors due to the limited number of studies and their perfect weight of 1. For the target variable performance expectancy, effort expectancy, TTF, and self-efficacy are the best predictors, and convenience and innovativeness are promising predictors. Health consciousness, social influence, reliability, and compatibility, although not classified as the best predictors, remain important because their weights exceed 0.700 and they are supported by multiple studies. For effort expectancy, facilitating conditions, innovativeness, self-efficacy, and compatibility are the best predictors, while reliability and TTF are promising predictors. For the target variable TTF, technology characteristics is identified as a promising predictor.

    Meta-Analysis

    The results of the meta-analysis are presented in and include all studies that reported standardized path coefficients or β values. All the best predictors identified in our study are statistically significant (P<.001), except for reliability (P=.49) as a predictor of intention, as well as some of the important and promising predictors. Notably, barriers (P=.46) is not a significant predictor of attitude. Health, technology anxiety (P=.78), financial cost (P=.16), and barriers (P=.84) are not significant predictors of behavioral intention. For actual behavior, social influence (P=.15), innovativeness (P=.28), health consciousness (P=.61), vulnerability (P=.31), and effort expectancy (P=.09) are not significant predictors. Privacy and security (P=.05) and barriers (P=.21) are not significant predictors of performance expectancy, while privacy and security (P=.29) and image (P=.06) are not significant predictors of effort expectancy. Finally, task characteristics (P=.12) is not a significant predictor of TTF.

    Table 4. Meta-analysis results calculated using a random-effects model and presented back-transformed.
    Dependent and independent variables r/ESa 95% CI Q statistic z score P value I2 statistic (%)
    Attitude
    Barriers 0.069 –0.113 to 0.251 107.606 0.743 .46 95.353
    Benefits 0.466 0.239 to 0.645 224.814 3.789 <.001 99.11
    Effort expectancy 0.286 0.23 to 0.342 153.69 9.549 <.001 86.987
    Facilitating conditions 0.527 0.344 to 0.671 874.478 5.067 <.001 99.657
    Performance expectancy 0.532 0.414 to 0.633 987.838 7.596 <.001 98.077
    Privacy and security –0.355 –0.618 to –0.093 354.157 –2.653 .008 99.435
    Social influence 0.342 0.182 to 0.483 305.084 4.069 <.001 98.033
    Behavioral intention
    Health 0.082 –0.049 to 0.21 18.448 1.233 .22 83.738
    Aesthetic appeal 0.319 0.266 to 0.371 1.314 11.027 <.001 0
    Attitude 0.573 0.454 to 0.672 1855.792 7.853 <.001 98.653
    Barriers –0.016 –0.171 to 0.139 469.927 –0.2 .84 97.446
    Benefits 0.309 0.092 to 0.497 688.109 2.757 .006 99.273
    Compatibility 0.123 0.081 to 0.165 109.339 5.729 <.001 96.342
    Effort expectancy 0.185 0.134 to 0.235 887.804 7.043 <.001 93.58
    Ethics 0.303 –0.232 to 0.698 143.458 1.117 .26 98.606
    Facilitating conditions 0.198 0.138 to 0.257 274.658 6.318 <.001 90.17
    Financial cost –0.08 –0.191 to 0.031 541.286 –1.414 .16 96.675
    Functional congruence 0.212 0.165 to 0.259 39.56 8.509 <.001 89.889
    Habit 0.377 0.307 to 0.444 495.521 9.72 <.001 98.789
    Health consciousness 0.298 0.222 to 0.371 4455.165 7.332 <.001 99.798
    Hedonic motivation 0.202 0.174 to 0.23 135.59 13.785 <.001 89.675
    Image 0.201 –0.215 to 0.556 62.391 0.947 .34 93.589
    Innovativeness 0.223 0.147 to 0.297 154.334 5.642 <.001 96.112
    Performance expectancy 0.339 0.295 to 0.381 1212.594 14.281 <.001 93.732
    Privacy and security –0.11 –0.202 to –0.018 361.354 –2.348 .02 93.912
    Reliability 0.148 –0.266 to 0.516 398.688 0.694 .49 98.997
    Self-efficacy 0.318 0.257 to 0.377 818.281 9.644 <.001 98.9
    Severity 0.12 0.005 to 0.231 8.992 2.04 .04 55.518
    Social influence 0.254 0.189 to 0.317 684.855 7.381 <.001 94.305
    Technology anxiety –0.013 –0.1 to 0.075 19.306 –0.279 .78 74.102
    Trust 0.294 0.177 to 0.403 666.839 4.769 <.001 98.35
    Vulnerability 0.101 0.009 to 0.191 19.657 2.153 .03 64.389
    Actual behavior
    Behavioral intention 0.563 0.427 to 0.674 1139.269 6.912 <.001 98.508
    Effort expectancy 0.353 –0.061 to 0.663 99.717 1.683 .09 97.994
    Facilitating conditions 0.863 0.706 to 0.939 10353.056 5.987 <.001 99.981
    Health consciousness 0.096 –0.267 to 0.436 1594.656 0.511 .61 99.812
    Innovativeness 0.232 –0.188 to 0.581 420.955 1.086 .28 99.525
    Performance expectancy 0.406 0.001 to 0.696 129.825 1.963 .05 98.459
    Social influence 0.31 –0.113 to 0.638 35.827 1.447 .15 94.418
    Vulnerability 0.216 –0.204 to 0.569 615.529 1.008 .31 99.675
    Performance expectancy
    Effort expectancy 0.38 0.302 to 0.454 587.488 8.873 <.001 95.064
    Barriers –0.137 –0.353 to 0.078 396.849 –1.252 .21 98.992
    Compatibility 0.222 0.041 to 0.388 85.162 2.403 .02 92.955
    Facilitating conditions 0.328 0.1 to 0.523 239.597 2.784 .005 98.748
    Health consciousness 0.188 0.021 to 0.345 140.742 2.206 .03 95.026
    Image 0.194 0.001 to 0.373 96.096 1.969 .049 94.797
    Innovativeness 0.279 0.049 to 0.481 115.684 2.364 .02 97.407
    Privacy and security –0.193 –0.387 to 0.001 919.956 –1.945 .05 99.456
    Reliability 0.309 0.171 to 0.435 82.88 4.258 <.001 87.934
    Self-efficacy 0.578 0.431 to 0.695 531.613 6.52 <.001 99.059
    Social influence 0.315 0.177 to 0.44 121.563 4.349 <.001 91.774
    Trust 0.254 0.046 to 0.441 92.595 2.382 .02 95.68
    Task-technology fit 0.678 0.544 to 0.778 278.406 7.539 <.001 98.563
    Effort expectancy
    Compatibility 0.318 0.208 to 0.42 35.374 5.441 <.001 85.865
    Facilitating conditions 0.436 0.343 to 0.521 24.225 8.321 <.001 79.36
    Image 0.147 –0.005 to 0.294 75.386 1.893 .06 97.347
    Innovativeness 0.376 0.287 to 0.458 133.654 7.786 <.001 95.511
    Privacy and security –0.074 –0.21 to 0.063 329.763 –1.06 .29 99.09
    Reliability 0.46 0.339 to 0.566 45.702 6.747 <.001 93.436
    Self-efficacy 0.604 0.529 to 0.67 1371.419 12.34 <.001 99.635
    Social influence 0.235 0.097 to 0.364 26.199 3.31 <.001 88.549
    Task-technology fit 0.883 0.843 to 0.914 910.714 17.155 <.001 99.78
    Task-technology fit
    Task characteristics 0.249 –0.07 to 0.522 460.432 1.537 .12 99.348
    Technology characteristics 0.654 0.429 to 0.803 123.033 4.729 <.001 97.562

    ar/ES: combined effect size (back-transformed from Fisher z).

    Combining Weight and Meta-Analysis Results: The Most Adopted Models in Research

    presents the weight and meta-analysis for the TAM, which explains how users adopt and use technology, emphasizing the influence of external variables on perceived usefulness (performance expectancy) and perceived ease of use (effort expectancy), which in turn affect attitudes, behavioral intentions, and actual technology usage [,]. Performance expectancy is the best and statistically significant predictor of both attitude (β=.532, P<.001) and behavioral intention (β=.339, P<.001). Attitude is the best predictor and has a significant impact on behavioral intention (β=.573, P<.001), while behavioral intention is the best and significant predictor of actual behavior (β=.563, P<.001). Effort expectancy is the best predictor and strongly influences performance expectancy (β=.380, P<.001) and attitude (β=.286, P<.001). Health consciousness (β=.188, P=.03), self-efficacy (β=.578, P<.001), innovativeness (β=.279, P=.02), and compatibility (β=.222, P=.02) are significant predictors of performance expectancy, each with a weight above 0.700. Innovativeness (β=.376, P<.001) and facilitating conditions (β=.436, P<.001) are significant predictors of effort expectancy, also with weights above 0.700.

    Figure 3. Weight and meta-analysis for the Technology Acceptance Model. Thicker paths indicate relationships with greater weight—that is, the strongest predictors (weight≥0.700). Higher weights are therefore represented by thicker lines. The numbers on the paths denote the mean β coefficients along with their significance levels.

    presents the weight and meta-analysis for the Unified Theory of Acceptance and Use of Technology (UTAUT), which explains how users adopt and use technology by assessing the impact of key predictors on behavioral intention and actual behavior [,]. Facilitating conditions (β=.863, P<.001) and behavioral intention (β=.563, P<.001) are significant predictors of actual behavior, while social influence is not. Behavioral intention is significantly influenced by performance expectancy (β=.339, P<.001), social influence (β=.254, P<.001), facilitating conditions (β=.198, P<.001), and habit (β=.377, P<.001), all with weights above 0.700. Effort expectancy (β=.185, P<.001) and hedonic motivation (β=.202, P<.001) are also statistically significant predictors of intention; however, financial cost is not.

    Figure 4. Weight and meta-analysis for the Unified Theory of Acceptance and Use of Technology. Thicker paths indicate relationships with greater weight—that is, the strongest predictors (weight≥0.700). Accordingly, higher weights are represented by thicker lines. The numbers on the paths denote the mean β coefficients along with their significance levels.

    presents the weight and meta-analysis combining the HBM and PMT, which explain individuals’ engagement in health-related behaviors. Both models emphasize the role of perceived threat, such as vulnerability and severity, and the evaluation of coping strategies, such as benefits, barriers, response efficacy, and self-efficacy, in shaping motivation to take protective or preventive actions [-]. The results indicate that, compared with other technology adoption models, the predictive power of health-related constructs is weaker and less consistent. Severity (β=.120, P=.04) and vulnerability (β=.101, P=.03) have weak weights on behavioral intention and exert a small but significant impact. Performance expectancy (β=.339, P<.001) and self-efficacy (β=.318, P<.001), which are also used in other technology-related adoption models, are the best predictors and have a significant impact on behavioral intention. Barriers do not have a significant impact (P=.84), whereas benefits exhibit a strong, statistically significant effect (β=.309, P=.006). It is also relevant to mention 2 additional predictors directly related to the health context but not part of the key components of these theories. First, health condition, which refers to the perception of having good health, has a weak and nonsignificant impact (P=.22) on intention. Second, health consciousness has a statistically significant impact on intention (β=.298, P<.001) but does not significantly influence behavior (P=.61).

    Figure 5. Weight and meta-analysis for the Health Belief Model and Protection Motivation Theory. Thicker paths indicate relationships with greater weight—that is, the strongest predictors (weight≥0.700). Accordingly, higher weights are represented by thicker lines. The numbers on the paths denote the mean β coefficients along with their significance levels.

    illustrates the TTF model, which examines how well technology aligns with users’ tasks to enhance perceived usefulness and adoption []. Only part of the theory is presented, as it remains understudied in the context of IoT in health care. The results show that TTF is a significant predictor of performance expectancy (β=.883, P<.001) while being classified as a promising predictor. The studies suggest that task characteristics do not have a significant impact on the fit between the task and the technology. However, technology characteristics are a promising and significant predictor (β=.554, P<.001).

    Figure 6. Weight and meta-analysis for the Task–Technology Fit model. Thicker paths indicate relationships with greater weight—that is, the strongest predictors (weight≥0.700). Accordingly, higher weights are represented by thicker lines. The numbers on the paths denote the mean β coefficients along with their significance levels. ns: not significant.

    presents the weight and meta-analysis of the Privacy Calculus Theory, which explores the trade-off between benefits and privacy []. The results indicate that trust (β=.294, P<.001) has a significant positive influence on behavioral intention and is classified as the best predictor. Privacy and security (β=–.110, P=.02) exhibits a significant negative effect on behavioral intention; however, the weight is small, suggesting that privacy and security concerns may not be a strong inhibitor of adoption.

    Figure 7. Weight and meta-analysis for Privacy Calculus Theory. Thicker paths indicate relationships with greater weight—that is, the strongest predictors (weight≥0.700). Accordingly, higher weights are represented by thicker lines. The numbers on the paths denote the mean β coefficients along with their significance levels.

    presents the weight and meta-analysis of the Theory of Planned Behavior, which posits that attitude, subjective norms, and behavioral control influence behavioral intention and, subsequently, behavior []. Both attitude (β=.573, P<.001) and self-efficacy (β=.318, P<.001) are the best and strongest predictors of behavioral intention.

    Figure 8. Weight and meta-analysis for the Theory of Planned Behavior. Thicker paths indicate relationships with greater weight—that is, the strongest predictors (weight≥0.700). Accordingly, higher weights are represented by thicker lines. The numbers on the paths denote the mean β coefficients along with their significance levels.

    presents the weight and meta-analysis of the Innovation Diffusion Theory, which explains the diffusion of new technologies through 5 dimensions []. Relative advantage (performance expectancy; β=.339, P<.001), complexity (effort expectancy; β=.185, P<.001), and compatibility (β=.123, P<.001) were found to be significant predictors. Image was not a significant predictor, and trialability and observability have not been sufficiently studied in the literature.

    Figure 9. Weight and meta-analysis for the Diffusion of Innovation theory. Thicker paths indicate relationships with greater weight—that is, the strongest predictors (weight≥0.700). Accordingly, higher weights are represented by thicker lines. The numbers on the paths denote the mean β coefficients along with their significance levels.

    Evaluation of Publication Bias

    This section evaluates the presence of publication bias and assesses the normality of the datasets used in the meta-analysis to ensure the reliability of the synthesized findings. Publication bias refers to the tendency for studies with significant or positive results to be more likely to be published, potentially skewing meta-analytic outcomes []. To ensure the robustness of our findings, we evaluated publication bias following the approach of Harrison et al [], which suggests that a single criterion can provide a more sensitive and appropriate test. We focused our analysis on one of the most widely examined relationships in our dataset: the relationship between performance expectancy and behavioral intention, which was reported in 77 studies ().

    Table 5. Studies (n=77) showing the effect size (z), SE (z), sample size, z score, Q component, significance of the paths between performance expectancy and behavioral intention, and the country.
    Subgroup and effect size (Z) SE (Z) Sample size z score Q component Significance Country
    Group 1
    0.268 0.051 387 5.965 2.350 Significant China
    0.604 0.050 397 13.455 26.161 Significant China
    0.387 0.080 158 7.933 0.258 Significant China
    0.165 0.046 469 3.718 15.264 Significant China
    0.086 0.053 357 1.908 23.979 Significant China
    0.230 0.051 386 5.113 5.199 Significant China
    0.727 0.065 243 15.616 34.684 Significant China
    0.224 0.036 769 5.122 11.555 Significant China
    0.186 0.058 304 4.074 7.740 Significant China
    0.149 0.113 81 2.702 3.039 Nonsignificant China
    0.333 0.071 201 7.018 0.037 Significant China
    0.472 0.050 406 10.530 6.373 Significant China
    0.205 0.056 325 4.504 6.462 Significant China
    0.198 0.054 345 4.362 7.588 Significant China
    0.217 0.086 139 4.372 2.267 Significant China
    0.950 0.084 146 19.257 52.169 Significant China
    0.406 0.065 237 8.704 0.827 Significant China
    0.471 0.072 197 9.914 3.014 Significant China
    0.519 0.065 239 11.139 7.032 Significant China
    0.180 0.047 462 4.039 12.734 Significant China
    0.105 0.071 201 2.223 11.509 Nonsignificant China
    0.289 0.040 624 6.567 2.068 Significant China
    0.286 0.064 247 6.145 0.907 Significant China
    0.377 0.039 668 8.591 0.615 Significant China
    0.446 0.051 386 9.927 3.830 Significant China
    0.157 0.043 552 3.560 19.651 Nonsignificant China
    0.258 0.047 450 5.774 3.535 Significant China
    0.401 0.096 111 7.772 0.324 Significant China
    1.040 0.077 171 21.531 80.869 Significant China
    0.586 0.028 1292 13.583 73.940 Significant China
    0.224 0.046 475 5.028 7.120 Significant China
    0.236 0.037 725 5.401 8.764 Significant China
    Group 2
    0.422 0.033 913 9.720 5.253 Significant Bangladesh
    0.586 0.075 181 12.212 10.210 Significant France
    0.132 0.062 267 2.855 12.172 Nonsignificant France
    0.202 0.125 67 3.478 1.342 Nonsignificant France
    0.633 0.070 206 13.382 16.646 Significant Germany, the United States, the United Kingdom, and Canada
    0.102 0.056 320 2.249 18.892 Nonsignificant Ghana
    0.319 0.039 645 7.273 0.469 Significant Ghana
    0.401 0.050 400 8.939 1.190 Significant India
    0.421 0.086 139 8.473 0.761 Significant India
    0.283 0.043 534 6.404 2.116 Significant India
    0.150 0.052 372 3.330 14.228 Significant India
    0.119 0.082 153 2.418 7.793 Nonsignificant India
    0.306 0.065 238 6.569 0.381 Significant India
    0.415 0.036 772 9.512 3.647 Significant Indonesia
    0.549 0.054 341 12.120 13.905 Significant Iraq
    0.245 0.069 212 5.191 2.162 Significant Italy
    0.413 0.066 233 8.841 1.017 Significant Japan
    0.265 0.071 200 5.587 1.307 Significant Jordan
    0.205 0.051 389 4.556 7.746 Significant Korea
    0.321 0.080 159 6.572 0.105 Significant Korea
    0.084 0.029 1158 1.948 79.454 Nonsignificant Korea
    0.239 0.060 280 5.209 3.172 Significant Nepal
    0.190 0.063 259 4.112 6.247 Significant Oman
    0.321 0.045 495 7.220 0.331 Significant Pakistan
    0.284 0.046 486 6.401 1.860 Significant The Kingdom of Saudi Arabia
    0.229 0.046 473 5.145 6.496 Significant The Kingdom of Saudi Arabia
    0.189 0.063 256 4.085 6.256 Significant The Kingdom of Saudi Arabia
    0.553 0.046 477 12.441 20.278 Significant South Korea
    0.574 0.045 487 12.910 24.967 Significant South Korea
    0.523 0.034 877 12.020 27.229 Significant South Korea
    0.518 0.097 110 10.014 3.141 Significant Switzerland
    0.485 0.055 335 10.682 6.343 Significant Taiwan
    0.393 0.061 268 8.519 0.575 Significant Taiwan
    0.149 0.047 458 3.346 17.727 Significant Taiwan
    0.321 0.091 125 6.340 0.082 Significant Taiwan
    0.245 0.113 81 4.436 0.807 Significant Taiwan
    0.688 0.065 243 14.796 28.070 Significant Turkey
    0.041 0.049 426 0.917 39.467 Nonsignificant Turkey
    0.230 0.099 106 4.416 1.398 Significant The United Arab Emirates
    0.141 0.050 407 3.143 17.070 Nonsignificant The United States
    1.157 0.052 376 25.681 244.930 Significant The United States
    0.167 0.060 277 3.619 8.873 Significant The United States
    0.266 0.092 120 5.227 0.756 Significant The United States
    0.848 0.047 450 19.012 112.411 Significant The United States
    0.321 0.056 322 7.045 0.215 Significant Worldwide

    To assess the presence of small-study effects and potential publication bias, a funnel plot was generated, and an Egger regression test was conducted. The funnel plot was constructed to visually evaluate publication bias [], with the SE plotted on the y-axis instead of sample size, as this enhances the detection of asymmetry []. In an ideal funnel plot, symmetry is expected, with smaller studies exhibiting larger SE scattered evenly on both sides of the pooled effect size. In , the studies display a somewhat asymmetrical distribution. Larger studies cluster near the combined effect size at the top of the funnel, while smaller studies show greater dispersion, potentially indicating publication bias or underlying heterogeneity. The trim-and-fill method estimates the number of potentially missing studies—often those with nonsignificant or negative results—and imputes them to generate an adjusted combined effect size. In this case, the imputed effect size is slightly smaller than the original estimate, suggesting that the observed meta-analytic effect may be modestly inflated due to the absence of smaller, less favorable studies.

    Figure 10. Funnel plot of studies examining the relationship between performance expectancy and behavioral intention.

    To statistically assess funnel plot asymmetry, we applied the Egger regression test [] to evaluate whether smaller studies tend to report larger effect sizes, which can indicate potential publication bias (see ). The test examines the relationship between effect sizes and their SEs to detect small-study effects. The results of the regression analysis showed that the intercept was not statistically significant (α=.381, P=.81), indicating no evidence of funnel plot asymmetry or publication bias. However, the slope coefficient was statistically significant (β=.327, P<.001), suggesting a positive association between study precision and effect size. While this does not indicate publication bias, it may reflect genuine heterogeneity among the included studies.

    The I2 statistic, which quantifies the proportion of total variation across studies attributable to true heterogeneity rather than sampling error [], revealed a very high degree of heterogeneity (I2>93%). This indicates that most of the variability in effect sizes reflects real differences across studies rather than random sampling error. These differences may arise from variations in study design, measurement tools, participant demographics, cultural contexts, or theoretical frameworks used across the included studies. To further explore the sources of heterogeneity, a subgroup analysis was conducted.

    Table 6. Egger‐type test for small‐study bias, using Excel’s Data Analysis Regression Tool, which uses standard ordinary least squares regression.
    Regression Coefficients SE t test (df) P value 95% CI
    Intercept (α) 0.381 1.544 0.247 (75) .81 –2.695 to 3.457
    Slope (β) 0.327 0.081 4.044 (75) <.001 0.166 to 0.489

    The subgroup analysis examined whether effect sizes differed between studies conducted in China and those conducted in other countries. By comparing studies from China with those from other regions, we aimed to evaluate potential regional biases, given that a large proportion of the included studies were conducted in China. The results, presented in , indicate that geographic location has little influence on the overall effect size, as both subgroups exhibit similar results. The combined effect size for studies conducted in China is 0.340 (95% CI 0.272-0.404), while for studies in other countries, it is 0.336 (95% CI 0.279-0.390). However, heterogeneity remained very high in both groups (China: I2=93%; other countries: I2=94%), indicating substantial variability even within each subgroup. Therefore, the subgroup analysis addresses concerns about potential bias from the large proportion of studies conducted in China, confirming that the results are largely stable across regions.

    Table 7. Subgroup comparison between China and the other countries in our sample.
    Subgroup Effect size P value 95% CI I2 (%)
    China 0.340 <.001 0.272-0.404 92.984
    Other countries 0.336 <.001 0.279-0.390 94.355

    Principal Findings

    The study of IoT adoption in health care reveals a diverse landscape of constructs and relationships, providing a comprehensive overview of the factors driving IoT adoption. This study synthesized findings from 109 papers and 115 datasets across various regions, including China, South Korea, the United States, and India, with most studies published in high-ranking journals. The combined weight and meta-analysis identified the best predictors and examined the adoption models most frequently used in IoT health care. highlights the strongest and most consistent predictors, integrating the results of both the meta-analysis and weight analysis.

    Figure 11. Best predictors identified in the weight analysis, along with their statistically significant mean β coefficients from the meta-analysis. The strength (weight) of each predictor is represented by the thickness of the line connecting the predictor to the target variable, with thicker lines indicating stronger predictors. The green box denotes constructs from the Unified Theory of Acceptance and Use of Technology framework, and the orange box denotes constructs from Technology Acceptance Model.

    Technology acceptance models, such as UTAUT and TAM, have been widely and successfully applied in the context of IoT in health care [,], which is unsurprising given that these are the most commonly used technology adoption models, highly cited, and successfully applied across diverse fields and contexts [,]. Compared with models such as UTAUT and TAM, where predictors such as performance expectancy and facilitating conditions consistently exhibit strong and reliable effects, the HBM and PMT models struggle to establish robust relationships with behavioral intention. This suggests that relying solely on health-related constructs or health behavior models may not be sufficient to explain health care technology adoption. Therefore, other individual factors, such as innovativeness, external factors, such as social influence, and technological factors, such as performance expectancy, may play a more decisive role [,]. Integrating context-specific health variables into robust models such as TAM or UTAUT can, however, provide additional insights. For example, individuals with strong health motivation or health consciousness tend to exhibit higher levels of performance expectancy from IoT health care technologies [,].

    The findings highlight several key factors influencing effort expectancy and performance expectancy, both of which are central to users’ attitudes toward technology. For effort expectancy, the most influential factor was self-efficacy, indicating that individuals who feel more confident in their ability to use the technology tend to perceive it as easier to operate [,]. Other important contributors include facilitating conditions, innovativeness, and compatibility, suggesting that a supportive environment, openness to new technologies, and alignment with users’ existing values and practices all help reduce the perceived effort required to use IoT in health care [,]. For performance expectancy, TTF emerged as the dominant influence, highlighting that when users perceive a strong alignment between the technology and the tasks they need to perform, they are more likely to view it as useful [,]. Additionally, health consciousness, self-efficacy, reliability, and compatibility played significant roles, emphasizing the importance of personal health concerns, confidence in usage, trust in the system’s dependability, and alignment with users’ existing values and practices [,]. Together, these findings underscore the relevance of both individual and contextual factors in shaping users’ perceptions of a technology’s usefulness and ease of use.

    Regarding individuals’ IoT health care technology adoption journey, the findings reveal that a positive attitude is crucial for successful adoption [,]. Efforts to cultivate positive perceptions can be made by leveraging the influence of important figures in individuals’ lives and by emphasizing the ease of use and the potential for improved health care outcomes [-]. When individuals hold a positive perception of IoT health care, they are more likely to intend to use it, which in turn positively influences actual usage [,]. To further enhance behavioral intention, the effectiveness of IoT health care solutions and the encouragement of health care professionals, family, and friends should be leveraged [,]. Additionally, individuals’ willingness to try new technologies plays a significant role, as more innovative users are more likely to adopt IoT solutions [,].

    Trust plays a decisive role in shaping behavioral intentions, reinforcing the notion that users are willing to trade some level of privacy if they perceive a system as secure and reliable []. Previous literature has found that individuals are often reluctant to adopt digital health or IoT technologies when they do not trust the provider [,]. This perspective may help contextualize the inconsistent results observed for privacy as a predictor of behavioral intention, as a notable proportion of studies reported nonsignificant relationships. Similarly, predictors such as barriers, vulnerability, and financial cost also exhibited higher frequencies of nonsignificant findings in our analysis. These inconsistencies may reflect how these constructs interact with—or are influenced by—the presence of stronger enabling factors. For instance, high perceived usefulness and trust may diminish the observed effects of barriers such as financial cost and privacy, as these factors may become less salient in users’ perceptions.

    Theoretical Implications

    This study makes several contributions to the theoretical understanding of IoT health care adoption by synthesizing findings from diverse quantitative studies and adoption models. The results reinforce the importance of established models such as TAM and UTAUT. They also suggest that integrating variables from other theories—such as health consciousness, innovativeness, and trust—into traditional technology acceptance frameworks can provide deeper insights into how individuals adopt IoT in health care. Behavioral intention is the most studied target variable, while attitude and actual behavior remain underexplored, indicating a gap in existing research on these critical components of the adoption process.

    Researchers should further investigate several promising but underexplored predictors that showed perfect weight, suggesting strong yet preliminary evidence of their relevance, to establish their broader applicability. For instance, regarding behavioral intention, the aesthetic appeal of health care technologies shows potential as a strong predictor. For actual behavior, facilitating conditions and social influence are promising predictors that warrant further exploration. In the case of the underexplored TTF theory, technology characteristics appear to be a promising predictor. For performance expectancy, convenience and innovativeness are promising predictors, while for effort expectancy, reliability and TTF show potential as predictors deserving additional investigation.

    By contrast, several predictors demonstrated limited or inconsistent relevance to the adoption of IoT in health care. For the outcome attitude, barriers did not have a statistically significant effect. For behavioral intention, predictors such as privacy and security, barriers, vulnerability, severity, compatibility, and financial cost produced inconsistent findings, with many studies reporting nonsignificant results. Specifically, health, technology anxiety, financial cost, and barriers were frequently not significant predictors of behavioral intention. When predicting actual behavior, variables such as social influence, innovativeness, health consciousness, vulnerability, and effort expectancy often failed to reach statistical significance. Regarding performance expectancy, both privacy and security and barriers were not consistently significant, and for effort expectancy, privacy and security and image did not show meaningful effects.

    Our findings indicate that regional differences alone do not fully explain the heterogeneity of results. Therefore, when applying the findings of this study, we recommend refining theoretical models to account for contextual factors and implementing practical strategies aligned with the strongest predictors identified, such as performance expectancy and self-efficacy, which can enhance adoption across different settings. Future adoption studies would benefit from incorporating context-specific factors that capture cultural and health care system differences, enabling a better understanding of how these contextual variables influence target outcomes, as either control or moderator variables.

    Several regions, particularly in Africa, South America, and Europe, remain underrepresented, highlighting a gap in the literature and the need for future research in diverse settings to improve the generalizability and equity of evidence regarding IoT adoption in health care. Finally, combining qualitative methods, such as interviews and focus groups, is recommended to gain deeper insights. This mixed methods approach can provide a better understanding of user perceptions and experiences, bridging the gap between quantitative results and the complex realities of technology adoption [,].

    Practical Implications

    The findings of this study provide actionable insights for practitioners, policy makers, and technology developers seeking to enhance IoT health care adoption. Key drivers—such as performance expectancy, self-efficacy, social influence, functional congruence, trust, habit, facilitating conditions, benefits, and innovativeness—consistently shape behavioral intention. For example, developers can focus on creating intuitive designs and user-friendly interfaces while emphasizing tangible performance benefits. Health care providers and policy makers can leverage trusted individuals, such as doctors and family members, to encourage adoption.

    The availability of resources and infrastructure that enable and support technology use—such as access to devices, technical support, internet connectivity, and integration with health care systems—plays an important role in adoption, as it reduces barriers for individuals starting to use IoT in health care [,]. Furthermore, adhering to robust data protection frameworks that ensure transparency from all entities handling health-related data aligns implementation with national and regional regulatory standards and fosters user trust [,]. Finally, targeting innovative individuals who are more likely to adopt IoT health care technologies or who already have the habits and skills to use them can further promote technology adoption.

    Limitations and Future Research

    This study has several limitations that warrant consideration. Our findings reveal a high level of heterogeneity, which is not fully explained by regional differences; therefore, the pooled estimates should be interpreted with caution. Future research should investigate additional factors that may account for this heterogeneity, such as study design, population characteristics, or model specification, and conduct moderator analyses to better address variability. Additionally, China accounts for a large proportion of the included studies, while several regions—particularly in Africa, South America, and Europe—remain underrepresented. As such, we caution against overgeneralizing our findings to all global contexts. Additionally, while this study synthesizes quantitative findings, it excludes qualitative research, which could provide deeper insights into contextual variability and user experiences influencing adoption. This exclusion may contribute to inconsistencies in the evidence, particularly for understudied predictors such as privacy concerns, perceived vulnerability, and financial cost, which often showed nonsignificant results. Future research should consider integrative literature reviews that include qualitative studies to better capture the nuanced interplay of individual, cultural, and technological factors.

    Conclusions

    Our comprehensive meta- and weight analysis of 115 unique datasets on IoT health care adoption revealed several significant predictors for the adoption of IoT health care technologies. Behavioral intention emerged as the most frequently studied target variable. By contrast, attitude, actual behavior, performance expectancy, effort expectancy, and TTF remain comparatively understudied, with very few paths examined more than 5 times. While adoption theories from the information systems field, such as UTAUT and TAM, are predominantly used, integrating context-specific factors or combining constructs from different theoretical models can provide deeper insights into IoT health care adoption and further support the adoption process.

    All the best predictors identified in our study were statistically significant, with the exception of reliability as a predictor of behavioral intention. For the target variable attitude, the strongest predictors were effort expectancy, performance expectancy, and social influence, while barriers did not have a statistically significant effect. Regarding behavioral intention, the most consistent and significant predictors were attitude, performance expectancy, habit, self-efficacy, functional congruence, reliability, and benefits. In addition, social influence, facilitating conditions, and trust demonstrated strong weights above 0.700, while aesthetic appeal was considered a promising predictor due to the limited number of studies. Conversely, variables such as privacy and security, barriers, vulnerability, severity, compatibility, and financial cost showed inconsistent results, with a high incidence of statistically nonsignificant findings. Specifically, health, technology anxiety, financial cost, and barriers were not statistically significant predictors of behavioral intention.

    For actual behavior, behavioral intention emerged as the best predictor, while facilitating conditions and social influence were considered promising. However, social influence, innovativeness, health consciousness, vulnerability, and effort expectancy did not reach statistical significance for behavior. Regarding performance expectancy, effort expectancy, TTF, and self-efficacy were the best predictors, followed by health consciousness, social influence, reliability, and compatibility as strong predictors, while convenience and innovativeness appeared as promising. Privacy and security and barriers, however, were not statistically significant predictors of performance expectancy. For effort expectancy, the most consistent predictors were facilitating conditions, innovativeness, self-efficacy, and compatibility, with reliability and TTF considered promising predictors; privacy and security and image did not show significant effects. Lastly, for the target variable TTF, technology characteristics emerged as a promising predictor, whereas task characteristics were not statistically significant.

    We thank the European Union’s Horizon Europe program and Fundação para a Ciência e a Tecnologia (FCT) for their support through the program UIDB/04152 – Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.

    This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152 – Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. It also resulted from the TwinAIR project, which received funding from the European Union’s Horizon Europe program under grant agreement No. 101057779.

    The datasets generated or analyzed during this study are available from the corresponding author (IV) on reasonable request.

    None declared.

    Edited by T Leung, A Coristine; submitted 08.Jul.2024; peer-reviewed by H Gandhi, J Walsh, GK Gupta, S Ashraf; comments to author 23.Dec.2024; revised version received 03.Mar.2025; accepted 31.Aug.2025; published 06.Jan.2026.

    ©Inês Veiga, Tiago Oliveira, Mijail Naranjo-Zolotov, Ricardo Martins, Stylianos Karatzas. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.Jan.2026.

    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.

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  • Johnson & Johnson unveils new data showing nipocalimab is the first and only investigational FcRn blocker with potential to reduce systemic lupus erythematosus (SLE) activity in a Phase 2 study

    SPRING HOUSE, Pa., (January 6, 2026) – Johnson & Johnson (NYSE: JNJ) today announced positive topline results from the Phase 2b JASMINE (NCT04882878) study of adults living with systemic lupus erythematosus (SLE) and the initiation of a Phase 3 program. The JASMINE study met the primary endpoint (percentage of patients achieving Systemic Lupus Erythematosus Responder Index [SRI-4]a composite response at Week 24 with statistical significance compared with placebo), and key secondary and exploratory endpoints, including those indicating the potential of nipocalimab for steroid sparing. Nipocalimab had a safety and tolerability profile consistent with previous Phase 2 studies, with no new safety signals identified.

    These data represent the first positive results of an investigational FcRn blocker treatment in this chronic, debilitating autoantibody-driven disease that impacts an estimated 3 to 5 million people worldwide, and 450,000 in the U.S.1,2 , Chronic symptoms of SLE include severe fatigue, joint pain and swelling, and rashes, including a hallmark butterfly-shaped facial rash.3

    JASMINE is a 52-week, multicenter, randomized, double-blind, placebo-controlled, parallel-group, dose-ranging study of nipocalimab in 228 adult participants with active SLE and the first positive study of an FcRn blocker for the treatment of active SLE.4

    “Systemic lupus erythematosus or SLE is a serious autoantibody-driven disease that can impact multiple organ systems, significantly reducing health-related quality of life for millions of people,” said Leonard L. Dragone, M.D., Ph.D., Disease Area Leader, Autoantibody and Rheumatology, Johnson & Johnson Innovative Medicine. “Many people living with SLE also face complications associated with long-term steroid use, underscoring the limitations of current treatment approaches and the critical need for immunoselective therapies that are safe, tolerable, and capable of reducing disease activity, while preserving immune function.”

    Full results from the JASMINE study will be presented at a future medical congress.

    Editor’s note:
    a. The SLE Responder Index 4 (SRI-4) is a composite measure used to assess treatment response in patients with SLE during clinical studies. It comprises criteria from three different internationally validated indices, SELENA-SLE Disease Activity Index (SELENA-SLEDAI), Physician Global Assessment (PGA) and the British Isles Lupus Assessment Group (BILAG) 2004.

    ABOUT JASMINE
    JASMINE (NCT04882878) is a 52-week, multicenter, randomized, double-blind, placebo-controlled, parallel-group, dose-ranging study of nipocalimab in 228 adult participants with active SLE and the first positive study of an FcRn blocker for the treatment of active SLE.4

    ABOUT SYSTEMIC LUPUS ERYTHEMATOSUS
    Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that occurs when the body’s immune system mistakenly attacks its own healthy tissues. This can lead to inflammation and damage in many parts of the body, including the skin, joints, heart, lungs, kidneys, and brain. SLE affects nine times more women than men, often striking initially between the ages of 15-44.7 In addition to systemic organ damage, other complications of SLE can include end-stage renal failure, scarring cutaneous lesions, neurological damage, and various forms of cardiovascular disease.5 People living with SLE often face reduced health-related quality of life, due to severe fatigue, mood disturbances, joint pain and swelling, and rashes, including the hallmark butterfly-shaped facial rash, as well as complications of long-term glucocorticoid use.3 Severe fatigue is the most widely reported and debilitating symptom of SLE, affecting up to 80% of people with SLE. SLE is the most common form of lupus, affecting 3 to 5 million people worldwide, approximately 70% of lupus cases.1,7 It is estimated that 450,000 people in the United States are affected by SLE.2

    ABOUT NIPOCALIMAB
    Nipocalimab is an investigational monoclonal antibody, designed to bind with high affinity to block FcRn and reduce levels of circulating immunoglobulin G (IgG) antibodies potentially without additional detectable effects on other adaptive and innate immune functions. This includes autoantibodies and alloantibodies that underlie multiple conditions across three key segments in the autoantibody space including Rare Autoantibody diseases, Maternal Fetal diseases mediated by maternal alloantibodies and autoantibody-driven Rheumatic diseases.8,9,10,11,12,13,14,15,16 Blockade of IgG binding to FcRn in the placenta is also believed to limit transplacental transfer of maternal alloantibodies to the fetus.17,18

    The U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) have granted several key designations to nipocalimab including:  

    • EU EMA Orphan medicinal product designation for hemolytic disease of the fetus and newborn (HDFN) in October 2019 and fetal and neonatal alloimmune thrombocytopenia (FNAIT) in April 2025
    • U.S. FDA Fast Track designation in HDFN and warm autoimmune hemolytic anemia (wAIHA) in July 2019, gMG in December 2021, FNAIT in March 2024 and Sjögren’s disease (SjD) in March 2025
    • U.S. FDA Orphan drug status for wAIHA in December 2019, HDFN in June 2020, gMG in February 2021, chronic inflammatory demyelinating polyneuropathy (CIDP) in October 2021 and FNAIT in December 2023
    • U.S. FDA Breakthrough Therapy designation for HDFN in February 2024 and for Sjögren’s disease in November 2024
    • U.S. FDA granted Priority Review in generalized myasthenia gravis in Q4 2024

    ABOUT JOHNSON & JOHNSON
    At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow and profoundly impact health for humanity.

    Learn more at https://www.jnj.com/ or at www.innovativemedicine.jnj.com.

    Follow us at @JNJInnovMed.

    Janssen Research & Development, LLC, Janssen Biotech, Inc. and Janssen Global Services, LLC are Johnson & Johnson companies.

    Cautions Concerning Forward-Looking Statements
    This press release contains “forward-looking statements” as defined in the Private Securities Litigation Reform Act of 1995 regarding product development and the potential benefits and treatment impact of nipocalimab. The reader is cautioned not to rely on these forward-looking statements. These statements are based on current expectations of future events. If underlying assumptions prove inaccurate or known or unknown risks or uncertainties materialize, actual results could vary materially from the expectations and projections of Johnson & Johnson. Risks and uncertainties include, but are not limited to: challenges and uncertainties inherent in product research and development, including the uncertainty of clinical success and of obtaining regulatory approvals; uncertainty of commercial success; manufacturing difficulties and delays; competition, including technological advances, new products and patents attained by competitors; challenges to patents; product efficacy or safety concerns resulting in product recalls or regulatory action; changes in behavior and spending patterns of purchasers of health care products and services; changes to applicable laws and regulations, including global health care reforms; and trends toward health care cost containment. A further list and descriptions of these risks, uncertainties and other factors can be found in Johnson & Johnson’s most recent Annual Report on Form 10-K, including in the sections captioned “Cautionary Note Regarding Forward-Looking Statements” and “Item 1A. Risk Factors,” and in Johnson & Johnson’s subsequent Quarterly Reports on Form 10-Q and other filings with the Securities and Exchange Commission. Copies of these filings are available online at www.sec.gov, www.jnj.com or on request from Johnson & Johnson. Johnson & Johnson does not undertake to update any forward-looking statement as a result of new information or future events or developments.

    Footnotes
    1 Tian, J., Zhang, D., Yao, X., Huang, Y., & Lu, Q. (2023). Global epidemiology of systemic lupus erythematosus: A comprehensive systematic analysis and modelling study. Annals of the Rheumatic Diseases, 82(3), 351–356. https://doi.org/10.1136/ard-2022-223035
    2 Wang, Y., Hester, L. L., Lofland, J., Rose, S., Karyekar, C.S., Kern, D.M., Blacketer, M., Davis, K., & Sheilds-Tuttle, K. (2022). Update on the prevalence of diagnosed systemic lupus erythematosus (SLE) by major health insurance types in the US in 2016. BMC Research Notes, 15, 5. https://doi.org/10.1186/s13104-021-05877-1
    3 Centers for Disease Control and Prevention. (2024). Symptoms of lupus. https://www.cdc.gov/lupus/signs-symptoms/. Last accessed: January 2026.
    4 ClinicalTrials.gov Identifier: NCT04882878. Available at: https://clinicaltrials.gov/study/NCT04882878. Last accessed: January 2026.
    5 National Institute of Arthritis and Musculoskeletal and Skin Disease. (2022) Systemic Lupus Erythematosus (Lupus). https://www.niams.nih.gov/health-topics/lupus. Last accessed: January 2026.
    6 Ahn, G.E., & Ramsey-Goldman, R. (2012). Fatigue systemic lupus erythematosus. International Journal of Clinical Rheumatology, 7(2), 217–227. https://doi.org/10.2217/IJR.12.4
    7 Lupus Foundation of America. Lupus facts and statistics. https://www.lupus.org/resources/lupus-facts-and-statistics. Last accessed: January 2026.
    8 ClinicalTrials.gov. NCT03842189. Available at: https://clinicaltrials.gov/ct2/show/NCT03842189. Last accessed: January 2026.
    9 ClinicalTrials.gov Identifier: NCT05327114. Available at: https://www.clinicaltrials.gov/study/NCT05327114. Last accessed: January 2026.
    10 ClinicalTrials.gov Identifier: NCT04119050. Available at: https://clinicaltrials.gov/study/NCT04119050. Last accessed: January 2026.
    11 ClinicalTrials.gov Identifier: NCT05379634. Available at: https://clinicaltrials.gov/study/NCT05379634. Last accessed: January 2026.
    12 ClinicalTrials.gov Identifier: NCT05912517. Available at: https://www.clinicaltrials.gov/study/NCT05912517. Last accessed: January 2026.
    13 ClinicalTrials.gov Identifier: NCT04968912. Available at: https://www.clinicaltrials.gov/study/ NCT04968912. Last accessed: January 2026.
    14 ClinicalTrials.gov Identifier: NCT04882878. Available at: https://clinicaltrials.gov/study/NCT04882878. Last accessed: January 2026.
    15 ClinicalTrials.gov Identifier: NCT06449651. Available at: https://clinicaltrials.gov/study/ NCT06449651. Last accessed: January 2026.
    16 ClinicalTrials.gov Identifier: NCT06533098. Available at: https://clinicaltrials.gov/ct2/show/NCT06533098. Last accessed: January 2026.
    17 Lobato G, Soncini CS. Relationship between obstetric history and Rh(D) alloimmunization severity. Arch Gynecol Obstet. 2008 Mar;277(3):245-8. DOI: 10.1007/s00404-007-0446-x. Last accessed: January 2026.
    18 Roy S, Nanovskaya T, Patrikeeva S, et al. M281, an anti-FcRn antibody, inhibits IgG transfer in a human ex vivo placental perfusion model. Am J Obstet Gynecol. 2019;220(5):498 e491-498 e499.


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