Category: 3. Business

Introduction

In the wake of the COVID-19 pandemic, there has been increasing demand for the remote delivery of health care services using information communication technologies (ICTs), including mobile phones, tablets, and computers [-]. Telehealth is defined as the use of ICTs to support and promote remote clinical health services, health education, public health, and health administration [,]. Telemedicine is a subset of telehealth that focuses on the use of ICTs for the “provision of health care services, including the exchange of medical information, diagnosis, treatment, and monitoring of patients who are not physically present with the health care provider” [].

The World Health Organization (WHO) classifies telemedicine services into one of two model types: (1) patient-to-provider, where telemedicine services are conducted between patients seeking health care services and health care providers, or (2) provider-to-provider, where telemedicine is conducted between 2 or more health care providers to provide specialized input or second opinions []. Telemedicine services may be delivered in real time (synchronously), where live interactive sessions are involved, or in a deferred mode (asynchronously), where data are stored and information is sent remotely through a remote client or patient monitoring, also known as telemonitoring. The main channels for providing telemedicine services include audio calls, SMS text messages, email, audio-video calls, smartphone or customized applications, and picture archive and communication systems [].

India (population of 1.4 billion) is home to some of the world’s earliest and largest telemedicine services []. Emerging first in the 1990s, early telemedicine services were designed and implemented by the Indian Space Research Organization (ISRO), using satellite communication to connect providers in frontline health facilities (“spokes” or peripheral hospitals) with specialists in tertiary hospitals (“hubs”) to deliver health care service remotely []. At the turn of the century, the ISRO expanded its partnership to include the Apollo private hospital network, a partnership that has evolved to include premier public sector facilities, including the All India Institute of Medical Science (AIIMS) New Delhi, the Postgraduate Institute of Medical Education and Research (PGIMER) Chandigarh, and the Sanjay Gandhi Postgraduate Institute of Medical Sciences, and additional private hospitals (Apollo, Aravind Eye Care, and Narayana Hrudayalaya) [-]. By 2015, the ISRO network had grown to include over 245 hospitals (205 district and rural hospitals and 40 superspecialty hospitals) across India [].

In the wake of COVID-19, additional telemedicine services have continued to emerge. Most notably, eSanjeevani, a national telemedicine service, was launched by the Government of India in early 2019. eSanjeevani includes both patient-to-provider and provider-to-provider telemedicine services and is currently operational in 31 states and union territories across India. Since September 2023, with the support of nearly 200,000 registered providers, eSanjeevani is reported to have served over 162 million patients through 1.08 million health and wellness centers (spokes) and 14,007 secondary or tertiary hospitals (hubs) [].

The growing digitization of health care services in India and elsewhere globally has highlighted the potential for telemedicine services to increase access to timely and appropriate care seeking, corresponding to improved health outcomes and cost savings to the individual and health system. Despite this potential, little is known about the varied typologies of telemedicine services providing in India, their design and model characteristics, scale of implementation, and the available evidence on their impact. Improved understanding of the services implemented to date, particularly at scale in India, may help to guide the efforts of future telemedicine services in other low- and middle-income countries where the disease burden is highest and the need for improved access to timely and appropriate health services is greatest.

This scoping review aims to describe the characteristics of large-scale telemedicine services initiated between 2000 and 2023 in India and to present an overview of the evidence available on these services. Study findings are anticipated to improve understanding of the vast expanse of telemedicine services offered in India and provide insights into the design, implementation, and available evidence on the impact of these telemedicine services.

Methods

Overview

This review adopted a scoping review methodology to map the breadth of telemedicine initiatives in India and generate insights into their design, implementation, and reported impact. In keeping with the objectives of scoping reviews, no formal assessment of risk of bias or methodological quality was undertaken []. The review was conducted in accordance with the framework proposed by Arksey and O’Malley [] and reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, as given in .

Search Strategy

A comprehensive and multisource search strategy was used to identify telemedicine services in India. The primary information sources were the 3 major scientific databases, such as Embase, PubMed, and Scopus. Additionally, a Google web search was conducted to identify gray literature and programmatic reports, and the Google Play Store was searched to capture relevant mobile health applications. The reference lists of included articles were also reviewed to identify additional eligible studies. This combined approach ensured that both published evidence and real-world implementations not indexed in traditional databases were included. Detailed search strategies for each source are provided in .

Eligibility Criteria

Telemedicine was defined as including (1) a health care expert (doctor, nurse, physical therapist, or nutritionist) who makes (2) decisions tailored to a specific patient profile, through (3) a digital solution, including phone, computer, or tablet. “Formal” telemedicine services were defined as digital communication sanctioned by the organization and used according to a protocol. Telemedicine services were further categorized based on the reported scale of their implementation and considered to be moderate to large in scale if they met one or more of the following criteria: (1) a minimum of 1000 app downloads or patients reached, catered to, or consultations conducted, and (2) implemented in >1 hospital or geographical location.

Included telemedicine services were restricted to those that included humans, were published in the English language between January 1, 2010, and July 4, 2023, and pertain to services in India. Studies were excluded if they (1) were 1-way direct-to-beneficiary applications that provide information only, (2) relied on informal technology use by providers, such as personal telephone calls or patient contact solely on publicly available chat applications (eg, WhatsApp), (3) focused on data capture, workflow support applications, clinical decision-making algorithms, or job aids, including those that use artificial intelligence to render a diagnosis or are used by providers to screen patients in the course of home visits, (4) pertained to e-training or e-mentoring services, or (5) self-monitoring services, including those involving artificial intelligence or chatbots. Articles focusing solely on the technical specification of internet connectivity, book reviews, and conference proceedings were also excluded.

Study Selection and Data Charting

Once identified, articles were imported to Covidence (Veritas Health Innovation Ltd), and the process of abstract screening was initiated using 2 independent reviewers and a third person to resolve conflicts. Full-text articles were screened by 2 independent reviewers and a third person to resolve conflicts. Data from the full-text articles were extracted into Microsoft Excel. To ensure alignment across reviewers with the data extraction, weekly meetings were held across the study team. Senior investigators additionally conducted spot checks of articles to review their classification and the data extracted.

Data Items

summarizes the extraction domains across three broad categories: (1) model type, (2) model characteristics, and (3) reach and impact. The model type includes the health delivery sector (public, private, or public-private partnership [PPP]) and the WHO classification type (provider-to-provider, patient-to-provider, or both). Model characteristics include key stakeholders, services provided, timing of delivery, service delivery channel, licensing provisions, monitoring, and learning and evaluation activities. Reach and impact include details on the scale of implementation and evidence on effectiveness where reported.

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Critical Appraisal of Evidence

Given that this is a scoping review and not a systematic evidence synthesis or meta-analysis, we did not assess the quality of evidence reported in individual articles. Rather, the goal of this scoping review was to identify the full range of telemedicine services, including those for which peer-reviewed articles have not been published. Findings from peer-reviewed articles on the effectiveness of telemedicine sought to provide a broad overview of the landscape of evidence across disparate types of research and areas of inquiry.

Synthesis of Results

Efforts to synthesize details on the model characteristics of the telemedicine service sought to follow the framework in . Efforts to collate evidence on effectiveness drew from the evaluation categories depicted in WHO’s guidance on the monitoring and evaluation of digital health interventions [].

Results

Overview

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram in provides a summary of the screening process. From the peer-reviewed article databases, 2366 articles were identified, and after the exclusion of 716 duplicates, the abstracts from 1650 articles were screened for eligibility. Of these, 1205 were excluded, and 445 articles were deemed eligible for full-text review. A total of 151 studies were included for the full-text review and data extraction, including 2 articles identified from the references of other articles.

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To identify unique telemedicine services, we categorized peer-reviewed articles by name and additionally reviewed the gray literature and Google Play Store. A total of 115 unique telemedicine services were identified (76 from the peer-reviewed literature and 39 from gray literature and the Google Play Store). Unique telemedicine services were further classified based on (1) scale and (2) reported use of specialized software. Among the 115 unique services, 89 (77%) were classified as being moderate to large in scale, and 26 (23%) were small. Large scale is operationalized as those services that met one or more of the following criteria: (1) a minimum of 1000 downloads, patients, or consultations, and (2) implemented in >1 hospital or geographical location. Among the 89 moderate- to large-scale services, 75 used specialized software and 14 used nonspecialized software, such as WhatsApp. The tables and figures that follow present extracted data for the unique moderate- to large-scale services that reported using specialized software (n=75).

Characteristics of Telemedicine Services

shows the distribution of moderate- to large-scale unique telemedicine services using specialized software initiated or launched between 2000 and 2023. On average, 3 new telemedicine services were initiated annually from 2000 to 2019, and the growth of new services occurred predominantly in the private sector. The start of the COVID-19 pandemic in 2020 corresponds to an increase in the number of new telemedicine services.

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Model Characteristics

presents summary characteristics of moderate- to large-scale telemedicine services using specialized software in India. Out of 75 services, 64% (48/75) were delivered by the private sector, while 19% (14/75) were public sector and 17% (13/75) were PPP. Nearly half (37/75) of the services were provided through a patient-to-provider model, 24% (18/75) provider-to-provider, and one-third (20/75) using both patient-to-provider and provider-to-provider models. Services were provided in real time (synchronous) for 69% (52/75), while 28% (21/75) of services delivered both synchronous and asynchronous services, and 3% (2/75) delivered only asynchronously. While most services (52/75, 69%) offered multispecialty care covering 2 or more health domains or conditions, one-third (23/75) focused on condition-specific care (eg, ophthalmology or mental health). All services in both public (14/75, 19%) and PPP (13/75, 17%) sectors were provided with limited (nominal charges for outpatient registration) to no fees. In the case of private sector services (n=48), service fees ranged from US $2.40 (INR 200) to US $7.21 (INR 600) per service, and for some services, monthly subscription fees ranging from US $18 to US $32 (INR 1500-3000) were charged depending upon the services beneficiaries subscribed to. For some private sector telemedicine services, beneficiary charges occurred indirectly through the purchasing of insurance and other employee wellness schemes.

Key Stakeholders

outlines the details of key stakeholders engaged in the implementation of moderate- to large-scale telemedicine services. The earliest telemedicine services involving the public sector were initiated by the ISRO with support of other government bodies, including the Department of Information Technology, Ministry of External Affairs, Ministry of Health and Family Welfare, and the state governments []. More recent telemedicine services have been led by the Ministry of Health and Family Welfare at the national level, in coordination with state governments for implementation (14/75, 19%). The public sector included both models of service delivery, that is, patient-to-provider (5/14) and provider-to-provider (2/14). In contrast, the majority of private sector services (48/75, 64%) were patient-to-provider (30/48), through one of two categories: (1) networks of hospitals (16/48, 33%) or (2) technology service providers (32/48, 67%) who created technology solutions. The latter included business-to-business for third-party health care providers (n=8) and business-to-consumer technology solutions for patients and providers (n=24).

All 75 (100%) telemedicine services had MBBS doctors or higher-level specialists as clinical providers, while 25% (19/75) included access to allied health services, 13% (10/75) to dentists, and 12% (9/75) to AYUSH (Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy—the 6 Indian systems of medicine) practitioners. Among beneficiaries, only 1 telemedicine limited services to pediatric patients, while the remainder (74/75, 99%) catered to patients of all age groups. Technology, monitoring and evaluation, and funding of the services were either not reported or limited. Detailed description of key stakeholders for each telemedicine services is provided in Multimedia appendix 3.

Scale of Implementation

The scale of implementation for the moderate- to large-scale telemedicine services using specialized software is summarized in . Services reported their scale of implementation using a wide range of parameters, and no common standard has yet been developed. Thus, we gather information on scale across the following parameters based on available information: (1) geographic areas (state and districts) of implementation, (2) number of registered providers, (3) number of spokes and hubs, (4) number of patients served or treated, (5) number of consultations (overall or daily) or prescriptions, and (6) number of downloads on the Google Play Store. Information for at least 1 scale parameter was reported in 75 telemedicine services.

Among public sector services, as of July 19, 2023, eSanjeevani reported the largest number of registered providers (n=185,100) and health facilities (>100,000 primary health clinics and >13,000 secondary and tertiary hospitals) and is operational across 31 states and union territories across India. The total number of patients served was reported to exceed 138 million, and over 10 million consultations were carried out from November 2019 to July 2023. Among PPP models identified, Apollo telehealth services reported providing services in over 350,000 telemedicine centers, Apollo clinics, and common service centers, and 73 Apollo hospitals across 14 states in India. From 2000 to 2023, Apollo services reportedly reached over 13 million patients and delivered over 16 million teleconsultations. Among private sector–only models (n=48), 17% (8/48) reported having conducted over 1 million consultations. Practo, a private sector service that launched in 2008, provides services through over 0.1 million doctor partners. provides a brief overview of the 5 largest telemedicine services in India that use specialized software.

Over a quarter (21/75, 28%) of the moderate- to large-scale services that used specialized software were being implemented in 1 state. The remaining services are implemented in multiple states—17% (13/75) in fewer than 10 states and 12% (9/75) in 10 or more states—or did not report any geographical location (4/75, 5%). All telemedicine service applications identified from the Google Play Store (28/75, 37%) were accessible in all states across India. However, for some of these (7/28, 25%), accessibility within states was limited to either major cities or certain parts of the state.

Beyond the distribution of telemedicine services across and within states, information on the number of “registered” or “active” providers was reported for only 20% (15/75) of services. For those services that reported this information, the number of active or registered providers ranged from 5 to 0.5 million, with 33% (5/15) reporting 100,000 or more providers. Telemedicine reach in terms of the number of patients served, treated, or “lives saved,” or the number of consultations or prescriptions provided, was reported for 61% (46/75) of services. For the 46 services that reported reach, 36 (78%) served less than 1 million patients or provided consultations, 8 services had between 10 and 20 million, and only 2 services reported more than >100 million patients served (eSanjeevani and Practo). Among the telemedicine applications in the Google Play Store, 61% (17/28) had fewer than 1 million downloads, and 39% (11/28) had more than 1 million, ranging up to >100 million downloads (4 telemedicine services).

Evidence on Effectiveness

We examined peer-reviewed research articles for evidence on the effectiveness of the 75 moderate- to large-scale telemedicine services. We considered an article to include evidence of effectiveness if it provided information on processes, outcomes, or impact. This included but was not limited to studies on reach, quality of care, economic evaluation, or provider or patient perceptions of the service. Details on the evaluation were extracted, including study design, methods, and findings ().

Evidence on effectiveness was available for 43% (32/75) of the services, reported across 84 articles. PGIMER Chandigarh’s telemedicine service was the most studied in terms of service effectiveness, with 10 articles published. The National Institute of Mental Health and Neuro Sciences Bengaluru telemedicine service was a close second with 7 articles, while 14 of the 32 services with evidence on effectiveness had just 1 published article covering this topic (). See for details by telemedicine service and article.

Evidence on Effectiveness Related to Health Outcomes From Analytic Studies

Given the large number of studies across a range of designs, we focus here on a synthesis of findings from the analytic research on health outcomes. Evidence of the effectiveness of telemedicine on health outcomes (which includes impact on patient access to care, diagnosis, and morbidity) was reported in 52 of 84 articles for 24 of 75 telemedicine service (), of which 18 articles for 11 services provided analytic evidence (see ). The remaining articles reported solely descriptive findings.

Within the set of 18 analytic articles on health outcomes, none were randomized controlled trials. A total of 7 were quasi-experimental studies on 3 services: the Pediatric HIV Telemedicine Initiative in Maharashtra [-], the World Health Partners’ Sky Program in Uttar Pradesh and Bihar [-], and Aravind Eye in Tamil Nadu []. The clinical management of children living with HIV in centers linked with the Pediatric HIV Telemedicine Initiative was better compared to nonlinked centers [-,]. Fewer patients were lost to follow-up at the centers with the Pediatric HIV Telemedicine Initiative, but there was no difference in the proportion of patients with delayed treatment once the telemedicine service reached its later phase of implementation []. The World Health Partners’ Sky Program showed no improvement in the quality and coverage of maternal health services at the population level [], no improvement in treatment for childhood diarrhea and pneumonia, nor reduced prevalence of these diseases before and after implementation [], nor did it change provider knowledge []. Opening an Aravind Eye telemedicine center staffed by mid-level (nonphysician) providers led to a significant increase in overall network visit rates and rates of eyeglasses prescriptions for the population living within 10 km of the new center [].

The 11 remaining analytical studies on health outcomes consisted of 5 observational cohort studies on 5 services [-], 5 observational cross-sectional studies on 5 services [-], and 1 observational case-control study []. Among the observational cohort studies, 2 found that telemedicine was associated with patient improvements; patient mental health scores significantly improved post telepsychiatry treatment in Goa [], and patients showed a significant reduction in hemoglobin A_1c (HbA_1c) test result from baseline to follow-up while receiving telemedicine support through the Diabetes Tele Management System at Jothydev’s Diabetes and Research Center in Kerala []. One found no significant difference in functional assessment of “overdentures” (dentures anchored to teeth or modified roots) fabricated by newly graduated students who were guided remotely through provider-to-provider telemedicine versus guided in person at a university teaching hospital []. Furthermore, 2 reported that telescreening for retinopathy of prematurity was suitable to assess incidence over time [,].

The observational cross-sectional studies found that the use of telemedicine for diagnosis was equal to in-person models or brought added benefit. The 2 found comparable levels of diagnosis between telemedicine and in-person care: school hearing tests conducted by doctors through a remote audiometer, Distortion Product Otoacoustic Emissions system, and video-otoscope compared to doctors in person [], and diabetic retinopathy screening conducted by doctors through Dr Mohan’s Diabetes Specialties Center’s teleophthalmology compared to doctors in person []. Comparing the diagnosis of head and neck tumors made in person by clinicians at Amrita Institute of Medical Sciences, Kochi, versus remotely by colleagues in the United States found high concurrence, low differential diagnosis, and some additional diagnoses []. Sankara Nethralaya’s telescreening model diagnosed a higher prevalence of diabetic retinopathy compared to the in-person ophthalmologist-based screening camp model and found more sight-threatening retinopathies []. Finally, a higher portion of children went for diagnosis referral to telediagnostic auditory brainstem response (ABR) compared to in-person ABR (97% taken to telediagnostic ABR appointment vs 80% taken to ABR appointment) [].

The observational case-control study compared virtual diabetes care using the Diahome app to hospital outpatient service use and found that app users had a greater reduction in HbA_1c (but higher triglycerides throughout) []. The remaining studies on health outcomes were descriptive, describing patient outcomes without a comparator.

Evidence on Costs and Cost-Effectiveness

Data on patient or provider costs for telemedicine services were reported in 21 studies. The predominant means of measuring costs was through structured surveys, which asked respondents about perceived savings of time and money [-], future willingness to pay for teleconsultation costs [], or actual costs incurred. Regarding the latter, in a limited number of studies, a broad range of cost-related outcomes were assessed, including distance traveled to seek care [,-], food and overnight charges [], consultation and clinical costs [,,,,-], waiting time [], and reported lost workdays [-]. These were used to collectively estimate costs and cost savings attributed to telemedicine services from a range of perspectives.

Costing analyses, which presented data on the costs of a single telemedicine service, were reported in 4 articles. These studies sought to present evidence on the telemedicine costs needed to establish the service [,,]. Data on the cost-effectiveness and cost-utility of telemedicine services were reported in 8 articles for 9 moderate- to large-scale services. The methods, including the perspective from which costs and effects were derived, the primary and secondary data sources, the analytic time horizon used, and sensitivity analyses conducted, varied widely across studies, which impeded efforts to draw cross-cutting syntheses of findings.

Discussion

Overview

Scoping review findings led to the identification of 2368 articles from which 151 studies and 115 unique telemedicine services were identified and further categorized based on their scale of implementation and use of specialized software. Among moderate- to large-scale services (n=89), 75 used specialized software in isolation or augmented with telephone calls, WhatsApp, Zoom, and other nonspecialized software. Of these 75 services, 64% (48/75) were in the private sector, and the rest were either public or in partnership with private actors. The patient-to-provider model was the model that nearly half (37/75) of the telemedicine used to deliver their services. Telemedicine services were provided in real time (synchronous) for 69% (52/75), and 28% (21/75) delivered both synchronous and asynchronous services. Evidence was available for 43% (32/75) of the services.

Efforts to differentiate telemedicine services based on their scale of implementation and use of software sought to narrow emphasis in a crowded space, removing the “‘noise” of services established ad hoc within a limited geography or health setting, or without the software arguably needed to scale or accommodate the structural and procedural access controls for handling sensitive personal health data. Use of nonspecialized software may stem from user preferences, wherein providers and patients are more comfortable using existing software already on their phones, or may be driven by specialized software shortcomings. In situations where the specialized software crashes or has limited functions (ie, is only suitable for booking appointments or cannot be used for bidirectional sharing of photos and documents), patients and providers may shift to nonspecialized software. This ongoing use of nonspecialized software has enabled telemedicine services to scale but may have some drawbacks. Using specialized software allows each consultation to be integrated with electronic medical records, enabling backend data on call duration and other parameters to be tracked. In cases where the use of nonspecialized software persists, facilities may want to ensure that providers use telemedicine only on official phones, thereby protecting patient data and ensuring separation of work and personal life for providers.

Data on the typologies of telemedicine models sought to distinguish between provider-to-provider, patient-to-patient, and hybrid models. The fact that public sector services used both models suggests that telemedicine is being operationalized as a health system–strengthening intervention in addition to improving patient access to services by the government. By comparison, in the private sector, the implementation of telemedicine services seemed to focus on the use of telemedicine to expand accessibility and reach.

We found that many departments in large hospitals such as AIIMS New Delhi, Jawaharlal Institute of Postgraduate Medical Education and Research, PGIMER Chandigarh, and Apollo used the hospital-wide telemedicine services in different ways, according to their department’s needs. For instance, at AIIMS New Delhi, we found that 6 departments were using telemedicine and that some had used it for over 6000 patients (eg, pediatrics) [], while others had used it for just 314 (eg, oncology palliative medicine) []. Some reported using only special software, while others reported augmenting this software with WhatsApp or telephone calls.

Study findings on the evolution of telemedicine services in India cement India’s place as a global leader in the use of technology for health. In other low-resource settings, the field is characterized by fragmentation and driven by private sector and nongovernment organization–led models with limited scale and reach. Nigeria is home to several telemedicine initiatives, including the World Telehealth Initiative [], which aims to expand health care access through a clinical mentorship service in Opoji, Nigeria, and Hudibia (established in 2013), which is an application-based solution that allows users to search and see doctors through videoconferencing or to book a face-to-face appointment []. In Ghana, a recent review of telemedicine services [] identified a small number of services, including Bima, which uses a direct-to-patient model to provide health advice and succinct health education to Ghanaians. Elsewhere regionally, HelloDoctor in South Africa [] and Babyl Rwanda [] are private sector models that aim to bolster access to medical doctors and nurses as well as a range of clinical and laboratory services directly to the phones of beneficiaries. Data on the uptake of these services are limited.

The wide breadth and variety of telemedicine services, including public and private sector–led and types of telemedicine models (patient-to-provider or provider-to-provider), render comparisons challenging. However, India is unique for a number of reasons. From a supply-side perspective, the government is investing heavily in national telemedicine services via eSanjeevani (established in 2019), which includes both a patient-to-provider and a provider-to-provider model. While there is limited evidence on the reach and impact of both eSanjeevani models, the service has scaled widely [] with support from Ayushman Bharat Digital Mission and other government initiatives. From a demand side, less than half of women in India report having access to a mobile phone that they themselves can use []. Further barriers to women’s use of technology [] are likely to limit the reach and use of patient-to-provider telemedicine services in India, particularly in rural areas. Emerging data on the limited uptake of eSanjeevani’s patient-to-provider model reinforces these challenges.

Limitations

The large volume of studies has necessitated that we narrow our focus to unique telemedicine services that are moderate- to large-scale and report using specialized software. The central challenge in reporting the scale was that few services publicly list information on the scale of implementation, including the number of active providers and consultations completed. Those that do have listings have varied definitions for key constructs. For example, unique consultations versus the number of patients treated, and active versus registered providers. While we extracted information on the reported evidence generation, given the volume and variety of methodological approaches undertaken, we have not taken into account the quality of evidence reporting.

Conclusions

The widespread proliferation of telemedicine services in India has much potential to improve access to and continuity of timely and appropriate care seeking for health. However, our findings highlight significant limitations in evidence generation and reporting. Future research is needed to bolster independent evidence gathering on the impact that telemedicine services may have in bolstering equitable access to timely, continuous health services of equivalent or better quality than face-to-face services. Further data on costs to beneficiaries, including any cost savings, as well as assurances that remote service delivery does not compromise beneficiary experiences, are needed.

Introduction

Physical activity is associated with numerous benefits among older people [-] and is a key recommendation for promoting healthy aging []. Advancing age is associated with increasing diversity and variability across a wide range of biological, physiological, functional, and performance measures—an expression of the growing disparity between biological and chronological age which typifies aging [,]. Accordingly, the effectiveness of current exercise (structured, purposeful physical activity) guidelines [,], which adopt a “one-size-fits-all” approach, is questionable, with protagonists emphasizing the need for a personalized approach [].

An additional limitation of current guidelines is the lack of detailed attention concerning fitness components other than cardiovascular fitness [,]. While guidelines specify the duration, intensity, and frequency of aerobic exercise, minimal guidance exists concerning optimizing balance, strength, and flexibility—all crucial factors for preserving functional integrity with advancing age. In particular, the absence of accurate individualized assessments of these components outside a laboratory setting hinders the transition from generalized to personalized exercise programs for older adults.

The potential contribution of artificial intelligence in shaping personalized medicine is of current interest, including the promotion of personalized physical activity [,]. Nonetheless, it remains a fact that current physical activity guidelines are manually formulated, generic, and focus on group rather than personalized exercises.

To address these limitations, we developed a novel home-based approach to personalized exercise programs for older adults, utilizing a simple smartphone that obviates the need for a laboratory or professional intervention. Through smartphone accelerometer and gyroscope sensors, we remotely assess key components of motor fitness, including balance, flexibility, and strength. Based on these assessments, a machine learning–generated personalized exercise program, tailored to each individual’s needs, was delivered via video directly on the smartphone.

We previously described the design, development, validation, and pilot study results [-]. Briefly, an interdisciplinary expert panel selected motor components and standard movement tests for remote fitness assessment. These were incorporated into a user-friendly smartphone app, which provided simple audiovisual instructions for self-testing, recorded the test results, and uploaded the raw data to a remote study database, where machine learning was used to create a unique fitness profile for each participant. In order to create personalized exercise programs, we developed a collection of exercises specifically designed for older adults, spanning the different movement abilities (balance, flexibility, and strength) and graded according to difficulty. According to the participants’ unique fitness profile, a tailored selection of exercises was chosen using machine learning, uploaded to the app, and remotely delivered to the study participant via the study app. With repeated fitness testing and ongoing data collection, the precision of machine learning for matching fitness profile to tailored exercise programs is constantly improving.

In this study, we tested the implementation of our approach in a randomized controlled trial. Our objective was to investigate the effectiveness of an 8-week, remotely delivered personalized exercise program based on individual fitness assessments, compared to either WHO general guidelines (active-control) or no intervention (control). We hypothesized that participants in the personalized experimental group would show greater improvements in balance, flexibility, and strength than those in the active-control and control groups. Specifically, we anticipated a reduction in body sway during balance tests, an increased range of motion in flexibility, and faster lifts of the body or arms in strength. The real-life implications and subsequent benefits of improvements across this range of motor fitness components are notable. Thus, for example, well-established evidence highlights that balance improvement is critical for fall prevention [,,], while flexibility [,] and strength [,] are essential for performing activities of daily living.

Methods

Study Design

A randomized controlled trial was conducted, with participants randomly assigned to three groups: (1) experimental group (personalized exercise); (2) active-control group (World Health Organization [WHO] general exercise guidelines); and (3) control group (no intervention).

Sample Size Predetermination

Based on a statistical power analysis using G*Power [], our target was to enroll 300 participants, with 100 in each of the 3 treatment groups (see more details in our protocol paper []). However, to maintain methodological rigor, we later conducted a more precise power analysis based on the actual number of participants included in the statistical analyses (see Results section).

Participants

The study was conducted in a community setting (private homes and independent living facilities) in central and northern Israel, including both urban and agricultural settlements. Recruitment included local flyers and lectures delivered by the principal investigator in community clubs, senior centers, cultural centers, or other local gathering places. Participant enrollment was carried out by the study team, which consisted of 6 testers and a study manager.

From a total of 317 volunteers, 239 community-dwelling, healthy older adults (155 women), aged 72.63 (SD 5.38) years, completed the 8-week study intervention, and 230 completed the full 12-week follow-up. Inclusion criteria were (1) age 65+ years, (2) independent living, (3) independent walking, (4) fluent in Hebrew, and (5) smartphone competency. Exclusion criteria included (1) cognitive impairment (Mini-Cog score <3) [], (2) any hospitalization (>24 h) or emergency room visit within past year for cardiac (heart failure, rhythm disorder, ischemia, valvular disease) or neurological conditions (dizziness, cerebrovascular, vestibular, progressive diseases affecting gait or balance), and (3) high fall risk (≥1 positive answer from 3-item test), validated in community-based exercise intervention and fall-prevention studies []. Data collection included demographics, self-reported habitual physical activity, depression status (Geriatric Depression Scale) [], and frailty index [].

Ethical Considerations

The study design, procedures, and informed consent were approved by the Hadassah Hebrew University Hospital Ethics Committee, Jerusalem, Israel (Trial ID 0074‐19-HMO), and were conducted according to the ethical standards of the Helsinki Declaration.

Informed Consent

Written informed consent was attained following a detailed explanation given by the study physician and included instructions on how to act in case of any possible medical complications arising either directly (eg, risk of falls and fractures, musculoskeletal pains, strained ligaments) or secondary to the exercise programs (eg, cardiac arrhythmias or angina). Participants were informed of their freedom to opt out.

Privacy and Confidentiality

Each participant was assigned a unique study identification number. The key linking these numbers to participant identities was stored separately by the principal investigator. All data were subsequently deidentified.

Compensation Details

Participants did not receive any compensation.

Randomization

We used a permuted block design [] with 2 stratification factors (age: 65‐74 and 75+ years; gender: men and women), each block included 6 participants, 2 per group. Block distribution was adjusted using the minimization principle [] in order to address randomization imbalance. More specifically, as there were 6 testers, and each tester had his or her randomization table. This process was supervised by an advanced student (EB).

At a certain point, we realized that few testers had the first participants on the control group, and the control group became a lot larger than the other groups. Based on the minimization principle, we changed the block distribution to 2 experimental, 2 active-control, and only 1 control (5 participants in a block).

Outcome Measures

The motor components chosen for remote fitness assessment were postural control (stability) while standing (static balance) or moving (dynamic balance), strength (muscle endurance) of upper and lower body, and range of motion of upper body (flexibility). The following standard movement performance tests were selected to assess these components:

1. Static balance: Leg stance (single leg stance left and right) for 10 seconds.
2. Static balance: Tandem stance—one foot directly in front of the other (left foot forward and right foot forward) for 20 seconds.
3. Dynamic balance: Tandem walk forward (10 steps) and tandem walk backward (10 steps).
4. Upper body flexibility (torso rotation): Seated position, holding a ball between thighs and baton in hands in front of chest for stabilization []; maximal torso rotation to left and right.
5. Upper body flexibility (arm flexion): Seated position, armless chair, back against wall; lifting straight arm (right and left separately) forward and up, trying to reach the ear.
6. Upper body flexibility (arm extension): Standing position, face towards wall, all front body against wall; lifting straight arm (right and left separately) backward as far as possible.
7. Upper body strength (arm strength): Seated position on an armless chair, a 0.5 kg weight for women and 1 kg for men attached to wrist; lifting straight arm (left and right separately) forward up to shoulder height as fast as possible 20 times and to the side 20 times.
8. Lower body strength (sit-to-stand): From sitting position, hands on waist, stand and sit 10 repetitions.

Instructions were incorporated into a smartphone app developed by Montfort Brain Monitor Ltd. Movement was captured by smartphone accelerometer and gyroscope sensors by attaching the mobile phone to the relevant body part using a simple band (see : tandem walk forward, arm flexion, arm extension, arm strength).

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Digital Markers

The specific digital markers (DMs) generated from the phone for each test are as follows:

• Balance tests (Tests 1‐3): Five DMs that assess body sway, with lower scores indicating less sway and better performance:
  1. Average linear acceleration (m/s²), generated by the accelerometer in the mediolateral direction.
  2. Average linear acceleration (m/s²), generated by the accelerometer in the anterior-posterior direction.
  3. Angular (radial) velocity, assessed in radians per second (rad/s), generated by the gyroscope in the mediolateral direction.
  4. Angular (radial) velocity, assessed in radians per second (rad/s), generated by the gyroscope in the anterior-posterior direction.
  5. Angular (radial) velocity, assessed in radians per second (rad/s), generated by the gyroscope in the superior-inferior direction.

• Torso rotation (Test 4): The angle (peak pitch) in the superior-inferior direction. A greater angle indicates a larger range of motion and better performance.
• Arm flexion (Test 5): The angle between the arm and the horizon (peak yaw in the anterior-posterior direction).
• Arm extension (Test 6): The angle between the arm and the horizon (peak yaw in the anterior-posterior direction). A greater angle indicates a larger range of motion and better performance.
• Arm strength (Test 7): Average duration for 20 repetitions, measured in seconds. A shorter duration indicates better performance.
• Sit-to-stand (Test 8): Average duration for each repetition, measured in seconds. A shorter duration indicates better performance.

Detailed information and graphical demonstrations of the DMs have been previously published [,].

Study Procedure

Data collection started in November 2020 and ended in September 2023. Following informed consent, demographic/clinical data collection, and randomization, baseline fitness (T0) was assessed using the study app. Fitness levels across the study measures were determined (low versus high), and assessment was repeated at 4 weeks (T1), 8 weeks (T2), and 12 weeks (follow-up T3). The assessment was conducted by 6 qualified physical activity teachers, each trained to use the app for testing participants individually and supervised by the study manager, a senior physical activity teacher. The app was installed on the teachers’ (testers’) smartphones, and the test results were automatically streamed to the database. All study participants (the personalized exercise group, the active-control group, and the control group) were tested. The testers and participants were blinded to the test results. Participants received weekly phone calls from the teachers to maintain contact and motivation. There were no issues of safety or other issues reported.

Intervention Groups

Personalized Exercise (Experimental Group)

Participants in the experimental group received their personalized exercise program based on their fitness assessment. The personalized program, including clear instructions regarding the performance of the exercises, was delivered to the participants’ personal smartphones immediately after the testing. The testers explained to the participants, on an individual basis, how to use the app on their personal smartphones for exercising. Following our pilot study [], and according to evidence concerning the advantages of exercising >3×/week [,], we instructed participants to exercise 5×/week for 8 weeks. Their video exercise program covered three target categories: (1) balance and lower body, (2) upper body flexibility, and (3) upper body strength exercises. Two difficulty levels, A (simple) and B (advanced), were matched according to fitness assessment. For an example of exercise displayed on a smartphone, please see . Additional examples have been described previously [].

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General Exercise (Active Control Group)

Participants were individually counseled and advised to exercise for 8 weeks according to official WHO guidelines []. Specifically, they were asked to perform leisure-type aerobic exercise for 150‐300 minutes or vigorous-intensity exercise for 75‐150 minutes/week. Examples such as walking, jogging, and cycling were given. In addition, they were asked to perform ≥3 sessions/week of multicomponent physical activity that emphasizes functional balance and strength training at moderate or greater intensity. They received the following examples of balance exercises: standing on toes, one-leg stance, walking while lifting the knee, walking backward, and side walking while bending and extending the knees. Examples of strength exercises included the following: (1) in a standing position—lifting the leg to the side (abduction), extending a straight leg backward, lifting a straight leg forward, sit-to-stand movements—and (2) against a wall—pushing the body away from the wall using the hands, lifting the arms to the side, optionally with dumbbells.

Control Group

Participants were advised to continue their normal routine. A personalized exercise program was offered after study completion.

Matching Fitness Level With Exercise Prescription for the Personalized Exercise Group—A Machine Learning Approach

Data from the pilot study [] served as a baseline for large-scale data collection, and the baseline (T0) fitness level of participants in the current study (low or high) was determined using machine learning principles. Based on the fitness level, the app determined the appropriate level of exercise difficulty (A or B) for each fitness component. For example, a participant in the personalized exercise group might have been assigned level A for balance exercises, level B for flexibility, and level B for strength. illustrates an example of a study participant’s unique fitness profile, based on the DMs, graphically displayed alongside the average profile for the entire study sample. Repeated fitness assessments at 4 weeks (T1) allowed for the personalized exercise program to be adjusted according to the updated DMs.

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Data Transformation

For comparability reasons due to different units of measurement, we transformed the DM data into normalized (z) scores. For balance measures, we calculated the mean of the 5 generated DM scores to one for each balance test. Results are presented as z scores.

Adherence to Program Recommendations: Adherers and Nonadherers in the Personalized Exercise Group—Post Hoc Distribution

Since participants needed to access the study app in order to watch and perform exercise videos, it was therefore possible to determine the actual and accurate measurement of adherence. Although they were instructed to exercise 5×/week, the actual adherence varied. To explore whether adherence influenced improvements, we conducted an exploratory analysis categorizing participants as adherers or nonadherers. The following cutoff points, along with their rationale, were examined:

• Stage 1: ≥3 sessions/week versus <3 sessions/week (based on official recommendations, whereby 3 sessions/week is considered optimal) [].
• Stage 2: ≥2.45 sessions/week versus <2.45 sessions/week (the median adherence score).
• Stage 3: ≥2 sessions/week versus <2 sessions/week (a lower threshold to determine the minimum frequency needed for fitness improvements).
• Stage 4: ≥1.5 sessions/week versus <1.5 sessions/week (an even lower threshold to determine the minimum frequency needed for fitness improvements).

A consistent trend favoring adherers over nonadherers emerged across all cutoff points (see ). The most notable differences were observed with the ≥2 sessions/week versus <2 sessions/week and ≥1.5 sessions/week versus <1.5 sessions/week cutoffs.

Ultimately, we selected 1.5 sessions/week as the final cut-off for two reasons:

1. This frequency suggested that significant improvements in balance, flexibility, and strength could be achieved with as few as 1.5 tailored exercise sessions per week.
2. It served as a criterion for distinguishing nonexercisers while still retaining a sizable proportion of participants (71.7%; see ) in the personalized exercise group.

Subsequently, we conducted statistical analyses using four groups: (1) personalized exercise adherers (n=66), (2) personalized exercise nonadherers (n=26), (3) general guidelines exercise (active-control; n=80), and (4) control (n=67).

Statistical Analyses

We applied statistical analyses to the 3-time measurements during the intervention period, T0, T1, and T2, and examined whether improvements observed after 8 weeks (T2) were maintained at 12-week follow-up (T3). Specifically, a mixed repeated measures ANOVA (3 test dates×4 groups) was conducted for each fitness component, and Eta squared (η²) was calculated to assess the effect size. Fisher LSD was used for pairwise post hoc analyses, and Cohen d coefficients were calculated to reveal the standardized differences between means when effects reached a significance level (P<.05).

To examine whether improvements were maintained at T3, we examined participants from the general group and personalized adherers who demonstrated improvement from T0 to T2 (between the baseline and the end of the 8-week intervention period). Improvement was defined as a change of ≥0.12 in z score (zT2-zT0), corresponding to the 55th percentile with normal distribution. More specifically, individuals with T2-T0 z scores ≥0.12 were considered “improvers.” Two-way ANOVA (2 test dates×2 groups) with repeated measures was applied on the outcome measures.

Attrition Rate (Percentage of Remaining Participants in Each Group)

Due to incomplete compliance during 1 or more measurements, or failure to upload measurements due to poor internet connectivity, specific data points for certain individuals were excluded. Additionally, scores greater than 2.5 SDs from the raw data means were omitted. As a result, the number of participants in each ANOVA (assessing the effect of the intervention on each fitness component) ranged from 189 to 231. The participation rate (%) for each group was calculated for the main outcomes (see ). χ² analyses revealed no significant differences. The power calculation was based on a sample size of n=189.

Results

Participants

illustrates the participant flowchart across the 3 original study groups. Of those who completed the 12 weeks, 90 were from the personalized exercise group, 78 from the general exercise group, and 62 from the control group.

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presents baseline data.

Power Analysis Based on the Post Hoc Distribution (Four Groups)

We used G*Power [] to perform power analysis for a 2-way ANOVA (3 test dates×4 groups) with repeated measures on the outcome measures. Our sample (n=189) provided 91% statistical power to find an interaction with small effect (Cohen f=0.1), 91% power to find group differences of moderate effect (Cohen f=0.25), and 97% power to find within measurements differences of small effect (Cohen f=0.1). All power analyses used a correlation of 0.685 among repeated measures because, among all our primary outcomes, this was the lowest (the highest was 0.856) and thus the most conservative value to use.

Results of the Statistical Analyses

describes the main results. Group×time interaction was significant for dynamic balance (mean tandem walk forward and backward, F_6,404=3.232, P=.004, η²=0.046; ). Pairwise analyses indicated significant improvements among personalized exercise adherers (Adherers) from T0 to T2 (M_diff0,2=0.228, P=.002, d=0.404) and group differences in favor of the Adherers in T2 (M_grp1,3=−0.357, P=.02, d=0.456; M_grp1,4=−0.383, P=.01, d=0.474; ). The interaction on static balance (mean of leg stance and tandem stance) was not significant, but group differences favored the Adherers at T1 (M_grp1,3=−0.375, P=.01, d=0.430; M_grp1,4=−0.361, P=.02, d=0.446; ).

Group×time interactions were revealed on both arm flexion (mean right and left, F_6,448=2.527, P=.02, η²=0.033) and arm extension (mean right and left, F_6,450=2.753, P=.01, η²=0.035; ). Follow-up pairwise analyses indicated significant improvement in the Adherers on arm flexion (left and right) from T0 to T2 (M_diff0,2=−0.227, P=.007, d=0.356) and on arm extension (left and right) from T0 to T1 (M_diff0,1=−0.221, P=.02, d=0.302) and from T0 to T2 (M_diff0,2=−0.210, P=.03, d=0.290; ).

Group×time interaction was also indicated on arm strength total (mean of lifting right arm forward, left arm forward, left arm to the side, and right arm to the side; F_6,424=2.394, P=.03, η²=0.033; ). Follow-up pairwise analyses indicated significant improvement among the Adherers on arm strength total from T0 to T1 (M_diff0,1=0.228, P=.008, d=0.438) and from T0 to T2 (M_diff0,2=0.217, P=.008, d=0.384; ).

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More results are presented in the tables below. Regarding balance, group×time interactions were significant for tandem stance left (left foot forward, η²=0.033), tandem walk forward (η²=0.039), tandem walk backward (η²=0.034), and balance total (mean of all balance scores, η²=0.040; ). Pairwise analyses indicated improvements (time differences) only in the Adherers from T0 to T2 (M_diff0,2=0.163, P=.009, d=0.259), with group differences favoring the Adherers at T1 (M_grp1,3=0.344, P=.007, d=0.470) and at T2 (M_grp1,3=0.399, P=.003, d=0.520, M_grp1,4=0.347, P=.01, d=0.480; ).

Regarding flexibility, group×time interactions were revealed on right arm flexion (η²=0.038), right arm extension (η²=0.032), left arm (mean flexion and extension, η²=0.035), and right arm (mean flexion and extension, η²=0.054; ). Pairwise analyses indicated significant improvements only among the Adherers on right arm flexion from T0 to T2 (M_diff0,2=−0.321, P=.002, d=0.425), on right arm extension from T0 to T1 (M_diff0,1=−0.264, P=.02, d=0.293), and from T0 to T2 (M_diff0,2=−0.262 , P=.03, d=0.309), on right arm (mean flexion and extension) from T0 to T1 (M_diff0,1=−0.205, P=.008, d=0.341) and from T0 to T2 (M_diff0,2=−0.283, P<.001, d=0.625), and on left arm (mean flexion and extension) from T0 to T1 (M_diff0,1=−0.192, P=.009, d=0.359; ). No change was observed on torso rotation ().

Concerning arm strength, a group×time interaction was observed for lifting right arm to the side (η²=0.034; ). Specific improvements were noted in the Adherers from T0 to T1 (M_diff0,1=0.247, P=.02, d=0.395) and from T0 to T2 (M_diff0,2=0.213, P=.02, d=0.327; ). Pairwise differences were observed for arm strength variables, although no interactions were found: the Adherers improved on right arm forward from T0 to T1 (M_diff0,1=0.271, P=.006, d=0.444) and from T0 to T2 (M_diff0,2=0.308, P=.002, d=0.466), on left arm forward from T0 to T1 (M_diff0,1=−0.215, P=.03, d=0.332) and from T0 to T2 (M_diff0,2=−0.266, P=.005, d=0.399), on left arm to side from T0 to T1 (M_diff0,1=0.201, P=.03, d=0.331) and from T0 to T2 (M_diff0,2=0.179, P=.04, d=0.272; ).

No change was observed in lower body strength (sit-to-stand; ).

Follow-Up

No group×time interaction was observed on any of the outcomes. Significant (P<.05) time effects were revealed, indicating significant deterioration in both groups—the personalized Adherers and the general groups—during the 4-week follow-up.

Discussion

Principal Findings

We examined the hypothesis that remotely delivered, personalized multicomponent exercise for older adults—based on a simple yet reliable and accurate smartphone motor fitness assessment and individually tailored using machine learning—can improve balance, flexibility, and strength among older adults, all without the need for a laboratory or professional supervision.

The results of this randomized controlled study demonstrated that a multicomponent personalized exercise program among older people was more effective when compared to active controls who were counseled to perform regular exercise according to WHO guidelines, as well as a passive control group (who received no intervention). Furthermore, improvements were achieved with as few as 1.5 tailored exercise sessions per week over 8 weeks and in many fitness measurements, even within 4 weeks.

The study tool has been refined from the initial prototype and proof of concept [-] and in its present form was proven to be sufficiently sensitive to measure both improvement and decline across the motor fitness measures, in as few as 4 weeks following the initiation or cessation of the exercise intervention, respectively.

The improvement in balance is of particular importance, as the relationship between balance impairment and falls [] and the positive effect of exercise on balance and fall prevention among older people is well documented []. However, exercise interventions typically involve group sessions in neighborhood clubs or physical therapy laboratories [], requiring a specialized trainer or therapist, and mostly target participants at risk of falls [,]. Balance control is multifactorial [,], and the novelty of the study tool presented in this study is not only its potential widespread application but also the fact that it includes 4 static and 2 dynamic balance tests, each comprising 5 DMs assessing body sway in all directions (mediolateral, anterior-posterior, and superior-inferior), offering a complete overview of the participant’s balance. Simultaneously, the balance-focused segment of our exercise programs addresses all components of balance, encompassing 5 subcategories: static, dynamic, vestibular, leg strength, and leg flexibility [].

Flexibility is a fitness component known to typically decline with advancing age, presenting a major contribution to reduced functioning []. Although amenable to improvement through focused exercise [], nonetheless, current guidelines provide only minimal detailed attention, and consequently, this element of fitness often remains neglected [,]. Our findings revealed significant improvement in flexibility in as little as 4 weeks of tailored exercise for upper body (arm) flexibility (both extension and flexion), which was most pronounced among the personal adherers, while measures of torso rotation remained unchanged. In all likelihood, this lack of observed change in torso flexibility is an artifact due to the technical difficulty measuring this vector with the smartphone.

The importance of upper body strength is notable with advancing age, with weakness associated with painful symptomatology [], upper limb dysfunction, and decreased performance measures []. It should be noted that upper extremity strength can be represented by several measurements, such as the widely used arm curl test [,]. We chose to focus on the shoulder joint because age-related decline in maximal upper extremity torque is greater at the shoulder than at the elbow and wrist joints [].

In contrast to the shoulder, we did not observe significant differences in leg strength, although a trend for improvement was seen in the intervention group. In our pilot study, we previously observed improvements in leg strength using sit-to-stand repetitions over a 30-second period [,]. However, to simplify testing, we modified this measure to a time score for 10 repetitions, which led to a ceiling effect. Importantly, leg strength is essential for functioning in older adults [,], and evaluating the impact of exercise on this component is crucial. Since the sit-to-stand repetitions (as many repetitions as possible over 30 s) were shown to be sensitive to changes following exercise, we plan to return to this method in future assessments.

Our study is unique in using a simple yet accurate smartphone platform to incorporate a remote assessment of balance, flexibility, and strength to serve as the baseline for a subsequent matched range of exercises, spanning these modalities of motor fitness. The existing models rely heavily upon fitness trackers, built-in accelerometers, and on occasion GPS to remotely monitor steps, endurance, and energy consumption. However, the critical elements of balance, flexibility, and shoulder strength are neglected, despite their crucial importance among older people. It is worth mentioning that recent evidence is emerging from the rehabilitation setting, supporting the use of smartphones for assessing balance or flexibility (range of motion) in older adults; however, mostly for highly specific conditions requiring clinical supervision [-].

Notably, while the platform demonstrated sufficient sensitivity to detect changes at a low threshold (1.5 sessions per week over 4 weeks), it remains uncertain whether this frequency is adequate to produce meaningful clinical health benefits. This concern is further emphasized by the small effect sizes observed—despite their statistical significance—and by the higher frequency recommended in WHO guidelines. Based on our findings, we cannot assert that this level of activity is sufficient for inducing clinical health improvements.

Similarly, the deterioration observed after the 4-week follow-up period without exercise was also subtle, suggesting that short-term inactivity may not necessarily lead to clinically relevant declines in fitness. This aligns with the broader detraining literature, which indicates that the rate of fitness loss is highly dependent on the initial training volume—encompassing factors such as intensity, duration, and frequency. Individuals with a higher training volume tend to experience a more gradual decline in fitness, whereas those with lower initial levels may exhibit more rapid losses [-].

In our study, the smartphone app was able to detect subtle yet significant improvements in fitness after 4 weeks of exercise, which then disappeared following 4 weeks of inactivity. While neither of these changes is likely to translate into meaningful clinical health effects, they underscore the application’s sensitivity in capturing even small fluctuations in fitness over time—both in improvement and decline. This capability highlights its potential as a tool for remotely monitoring fitness trends that might otherwise go unnoticed outside of laboratory settings.

An interesting finding of our study is the notable relationship between participants’ habitual physical activity and sedentary time before starting the intervention and their subsequent adherence to the exercise regimen. Significant differences were observed between adherers and nonadherers, with adherers engaging in more habitual physical activity and nonadherers spending more time sedentary. It is important to note that the classification into adherers and nonadherers was made post facto, only after the intervention ended. This suggests that nonadherence to physical activity, often associated with a more sedentary lifestyle, may reflect an underlying behavioral predisposition that persists even when individuals voluntarily enroll in an exercise program.

Study Limitations

Several limitations to our study deserve mention. First, the rate of adherence to the exercise program was accurately measured via the smartphone among the interventional group, while we had to rely upon self-reported data among the controls. Attempts to categorize the active control group into adherers and nonadherers based on self-reports, using the same cutoff points as described for the personalized group, showed no clear trends. To address this limitation, we compared the overall percentage of improvers in the intervention group to those in the active control group over the 8-week program, irrespective of the degree of adherence. Compared to the controls, the intervention participants showed higher rates of improvement for static balance (46% vs 33.3%, P=.07), dynamic balance (53.6% vs. 28.4%, P=.002), arm flexion (52.8% vs 43.4%, P=.48), and arm extension (46.1% vs 34.6%, P=.30).

Second, in the current study, in order to generate accurate data for the study, the trained study personnel performed the testing of participants. However, the ultimate goal of the application is that participants will be able to assess themselves (or possibly with the assistance of a lay person, eg, family member).

Another limitation is the sample’s bias toward participants from higher socioeconomic status. As this is a randomized controlled study, this bias does not affect the main outcome—the improvement of the intervention group over the controls. However, it may impact the generalizability of the study results to other populations.

In addition, in the current study, we applied only 2 levels of difficulty for each of the 3 movement components. As large-scale data accumulate, there is potential to expand and further define more variations of movement profiles.

Furthermore, physical fitness may include more components than the ones included in our study. For example, for assessing strength, while the selected tests did represent the major muscle groups, nonetheless, not all muscle groups were included. We also did not include other components such as eye–hand coordination or fine motor abilities. However, we selected the most appropriate tests for assessing motor and physical fitness in old age, taking into consideration medical aspects and safety precautions (for more information on designing the tests, see []).

The interaction with the technology—the smartphone—may also be problematic. For example, using the smartphone screen was inconvenient for some participants, and larger screens would improve the ease of performing the exercises. This could be solved by connecting the smartphone to a large screen. In addition, the videoed exercise program was running in a continuous manner on the phone screen; however, if the participant wanted to repeat an exercise before moving to the next one, or to stop for some reason, he or she had to change his or her current position, approach the phone, stop the program, and restart it again.

It may be argued against the smartphone sensors that there are settings where more accurate assessments may give more accurate results. Although applicable for research, these settings are inaccessible to the general population as they are scarce and expensive to use. Prior work [,] has already demonstrated that smartphones offer a reliable tracking of motor functions and therefore offer a sufficient, even if not perfect, means for evaluation in large scales.

The study sample was generally healthy at the time of enrollment, as assessed by the study physician. Enrolled participants were cognitively intact, free from any hospitalization in the last year for any cardiac or neurological reason, independent in daily function, able to walk without assistance, and with a low fall risk. Furthermore, we included a 41-item frailty assessment (see ), which confirmed very low levels of frailty across all participants. Although comorbidities or other current health conditions were not formally assessed, nonetheless, the study sample’s health profile of high cognitive function, independent functional and mobility status, very low frailty levels, and no recent cardiological or neurological illness for hospitalization was suggestive of an overall health status.

Using Artificial Intelligence for Extending and Refining the Tailored Exercise Program

Generating optimal personalized exercise program should rely not only on assessing the level of fitness but also considering basic aspects of the individual health status and behavior patterns. In line with personalized genomics and big data accumulation, a possible future upgraded version of this application might be used in conjunction with data drawn from genetic, molecular, clinical, social, and behavioral domains in order to achieve an optimal prescription of personalized exercise programs.

Furthermore, large-scale data will enable an in-depth analysis of individual movement capacities, a more accurate assessment of a wide range of fitness and mobility parameters, and, subsequently, more specific personalized exercise programs. For instance, when assessing balance in individuals with stability issues, it is beneficial to analyze the specific directions of sway—mediolateral, anteroposterior, and superior-inferior. Similarly, an arm strength test can be broken down into separate actions of “lifting the hand up” and “lowering it down,” while the sit-to-stand test can be divided into “getting up” and “sitting down.” Accurately localizing movement impairments has important clinical implications.

The potential ramifications of this study are multifold. First, we provide a model whereby a home-based, highly detailed remote analysis of fitness and mobility parameters provides objective quantitative data as a basis for subsequent tailored exercise interventions at the individual level. Furthermore, the sensitivity of the remote sensors was shown to be sufficient to enable an adaptive, precise, and updating exercise program, determined by the participants’ progress over time. In addition, the continually updating database serves to constantly improve the machine learning precision of decisions concerning the selection of appropriate exercises. Second, our study included only healthy, independent adults aged 65 years and over. We provide evidence to support a platform that has the potential to be adapted to meet the specific needs and abilities of various healthy and patient populations. Specifically, among older people, it is plausible that targeted and individualized exercise interventions are likely to play a beneficial role in improving intrinsic capacity and resilience, moderating frailty and deconditioning. Similarly, individualized programs can be further refined to address specific needs of populations with mobility limitations, such as those at risk of falls or confined to wheelchairs, as well as the potential applicability among participants with cognitive impairments.

Summary and Conclusions

In this randomized controlled study, we examined a novel home-based approach for personalized exercise programs for older adults, utilizing a simple smartphone that obviates the need for a laboratory or professional intervention. Through smartphone accelerometer and gyroscope sensors, we remotely assessed key components of motor fitness, including balance, flexibility, and strength. Based on these assessments, a machine-learning-generated personalized exercise program, tailored to each individual’s needs, was delivered via video directly on the smartphone. The results demonstrated that a multicomponent personalized exercise program was more effective when compared to active controls who were counseled to perform regular exercise according to WHO guidelines, as well as a passive control group who received no intervention. Furthermore, improvements were achieved with as few as 1.5 tailored exercise sessions per week over 8 weeks and in many fitness measurements, even within 4 weeks. The remote delivery of user-friendly multicomponent exercise programs to older people has the potential for widespread health benefits. Given the potential of this approach to be extended to various groups of older adults, including those with mobility and cognitive impairments, and considering the advantages of cost-free digital technology (assuming widespread smartphone ownership), health care providers may adopt this approach to provide home-based strategies for health promotion.

Global Sovereign Debt Roundtable – 5th Cochairs Progress Report

October 15, 2025

Washington, D.C – The Global Sovereign Debt Roundtable (GSDR) met today and reviewed progress on the work to improve debt restructuring processes and timelines, and to help address debt vulnerabilities. Participants also discussed priority areas for the work going forward. At the end of the meeting, the International Monetary Fund Managing Director Kristalina Georgieva, World Bank Group President Ajay Banga, and Finance Minister of South Africa, G20 Presidency, Enoch Godongwana, co-chairs of the GSDR, issued the GSDR 5^th Cochairs Report as well as the compilation of technical issues discussed by the GSDR so far.

The GSDR brings together debtor countries and official and private creditors with the objective to build common understanding among key stakeholders on debt sustainability and debt restructuring challenges, and ways to address them.

 

 

 

 

 

• Strengthens Viatris’ Presence in Japan With the Addition of Two Innovative Assets Targeting Areas of Significant Unmet Medical Need
• Leverages Existing Infrastructure and Expertise in Central Nervous System Therapy Area
• Aligned with Strategy to Target Accretive Regional Business Development Opportunities

PITTSBURGH, Oct. 15, 2025 /PRNewswire/ — Viatris Inc. (Nasdaq: VTRS), a global healthcare company, today announced it has acquired Aculys Pharma, Inc., a clinical stage biopharmaceutical company focused on commercializing innovative treatments for neurological conditions. Viatris received rights to develop and commercialize pitolisant and Spydia^®, two assets in the Central Nervous System (CNS) therapy area, further expanding Viatris’ portfolio of innovative products in Japan.

As part of the transaction, Viatris has acquired exclusive development and commercialization rights in Japan for pitolisant, a selective/inverse agonist of the histamine H3 receptor. Based on the strength of recent Phase 3 clinical trial results in Japanese patients and the positive benefit-risk profile established globally, Viatris is on track to file for marketing approval from the Ministry of Health, Labour and Welfare (MHLW) of Japan for the treatment of excessive daytime sleepiness (EDS) or cataplexy in adult patients with narcolepsy and for the treatment of excessive daytime sleepiness associated with obstructive sleep apnea syndrome (OSAS) by the end of 2025.

The transaction also includes exclusive rights in Japan and certain other markets in the Asia-Pacific region for Spydia Nasal Spray, which was approved in Japan in June 2025 for the treatment of status epilepticus.

“The acquisition of Aculys Pharma leverages our deep commercial infrastructure in Japan and longstanding expertise in CNS, positioning us to bring these innovative treatments to more patients in need,” said Corinne Le Goff, Chief Commercial Officer, Viatris. “The addition of pitolisant and Spydia to our portfolio of innovative products is strategically aligned with our commitment to grow in areas where we can make the greatest impact and is a great example of our business development strategy designed to complement our core strengths in markets across the world.” 

This acquisition further expands Viatris’ portfolio of innovative products in Japan which includes Effexor for the treatment of generalized anxiety disorder (GAD) which is under regulatory review, selatogrel in Acute MI, Nefecon in IgA nephropathy, and cenerimod in systemic lupus erythematosus (SLE), all of which have pivotal Phase 3 trials currently on going and Tyrvaya in dry eye disease for which a Phase 3 trial is anticipated to start in 2026.

Terms of the Transaction
Under the terms of the acquisition agreement, Viatris has made an upfront payment to Aculys Pharma shareholders as consideration for the acquisition, with additional consideration contingent upon the achievement of specified regulatory and commercial milestones, and royalties on net sales.

About Pitolisant
Pitolisant is an antagonist/inverse agonist that selectively binds to the histamine H3 receptor, an autoreceptor located in the presynaptic region of the histamine-containing neurons in the human brain that plays a critical role in regulating sleep and wake rhythm. The drug was approved by the European Medicines Agency (EMA) for the treatment of narcolepsy with or without cataplexy in 2016 and for the treatment of excessive daytime sleepiness associated with OSAS in 2021. In 2019, the U.S. Food and Drug Administration (FDA) approved pitolisant under the brand name Wakix^® for the treatment of excessive daytime sleepiness associated with narcolepsy and cataplexy associated with narcolepsy in 2020. As of the end of 2023, pitolisant had obtained regulatory approval in 38 countries including the U.S. and the EU for the treatment of narcolepsy, and in 29 countries in the EU for the treatment of OSAS.

Positive Pivotal study results in Japanese patients were recently achieved in both narcolepsy and OSAS. In the narcolepsy Phase 3 trial, the primary endpoint of improvement in excessive daytime sleepiness (EDS) compared to a placebo group using the Epworth Sleepiness Scale (ESS) was met. Statistically significant difference in ESS was observed between the two groups. Furthermore, the key secondary endpoint of the frequency of cataplexy attacks showed a suppression effect comparable to that observed in prior global Phase 3 trials. No serious adverse events were noted, and the safety and tolerability results were consistent with global clinical trials.

The OSAS Phase 3 trial evaluated the effect of pitolisant in Japanese patients with OSAS who were experiencing residual EDS despite treatment with CPAP therapy. At the end of the 12-week treatment period, patients receiving pitolisant scored lower on the ESS used to measure EDS compared to those in the placebo group and this difference was statistically significant (p=0.007). Additionally, safety and tolerability were consistent with results from global clinical studies.

About Spydia (diazepam)
In Japan, Aculys Pharma received marketing approval for Spydia Nasal Spray 5 mg, 7.5 mg, and 10 mg for the treatment of status epilepticus in patients 2 years or older in June 2025. This is the first intranasal anti-seizure medication approved in Japan for the treatment of status epilepticus or seizures with potential progression to status epilepticus. It is also the first rescue medication approved for adults for out-of-hospital use.

This drug was developed by Neurelis, Inc. in the US and Aculys Pharma obtained exclusive development and commercialization rights in Japan and certain markets in the Asia-Pacific region, including Australia, Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, New Zealand, Philippines, South Korea, Thailand and Vietnam. In 2020, the FDA approved diazepam nasal spray under the brand name Valtoco^® for the acute treatment of intermittent, stereotypic episodes of frequent seizure activity that are distinct from a usual seizure pattern in patients with epilepsy aged 6 years and older. In April 2025, the FDA extended the indication to include patients aged 2 years and older. Diazepam has been used for approximately 60 years in Japan, primarily in injectable form as a treatment for epileptic seizures. 

About Viatris
Viatris Inc. (Nasdaq: VTRS) is a global healthcare company uniquely positioned to bridge the traditional divide between generics and brands, combining the best of both to more holistically address healthcare needs globally. With a mission to empower people worldwide to live healthier at every stage of life, we provide access at scale, currently supplying high-quality medicines to approximately 1 billion patients around the world annually and touching all of life’s moments, from birth to the end of life, acute conditions to chronic diseases. With our exceptionally extensive and diverse portfolio of medicines, a one-of-a-kind global supply chain designed to reach more people when and where they need them, and the scientific expertise to address some of the world’s most enduring health challenges, access takes on deep meaning at Viatris. We are headquartered in the U.S., with global centers in Pittsburgh, Shanghai and Hyderabad, India. Learn more at viatris.com and investor.viatris.com, and connect with us on LinkedIn, Instagram, YouTube and X.

About Aculys Pharma, Inc.
Aculys Pharma is a clinical stage biopharmaceutical company that is pioneering ways to eliminate drug lag/drug loss in Japan and is working to resolve social issues related to neurological and psychiatry diseases. Its corporate name was created from the philosophy of serving as a “Catalyst to Access”. Aiming to act as a bridge for innovative medical care in the field of neuropsychiatry, Aculys Pharma develops and commercializes novel pharmaceuticals introduced from the US and European countries and provides innovations for better medical care to patients, their families, healthcare professionals, and society.

Forward-Looking Statements
This press release includes statements that constitute “forward-looking statements.” These statements are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Such forward-looking statements may include statements regarding Viatris completes acquisition of Aculys Pharma including exclusive rights to pitolisant in Japan and to Spydia® in Japan and certain other markets in the Asia-Pacific region; strengthens Viatris’ presence in Japan with the addition of two innovative assets targeting areas of significant unmet medical need; leverages existing infrastructure and expertise in Central Nervous System therapy area; aligned with strategy to target accretive regional business development opportunities; based on the strength of recent Phase 3 clinical trial results in Japanese patients and the positive benefit-risk profile established globally, Viatris is on track to file for marketing approval from the Ministry of Health, Labour and Welfare (MHLW) of Japan for the treatment of excessive daytime sleepiness (EDS) or cataplexy in adult patients with narcolepsy and for the treatment of excessive daytime sleepiness associated with obstructive sleep apnea syndrome (OSAS) by the end of 2025; the acquisition of Aculys Pharma leverages our deep commercial infrastructure in Japan and longstanding expertise in CNS, positioning us to bring these innovative treatments to more patients in need; the addition of pitolisant and Spydia to our portfolio of innovative products is strategically aligned with our commitment to grow in areas where we can make the greatest impact and is a great example of our business development strategy designed to complement our core strengths in markets across the world; this acquisition further expands Viatris’ portfolio of innovative products in Japan which includes Effexor for the treatment of generalized anxiety disorder (GAD) which is under regulatory review, selatogrel in Acute MI, Nefecon in IgA nephropathy, and cenerimod in systemic lupus erythematosus (SLE), all of which have pivotal Phase 3 trials currently on going and Tyrvaya in dry eye disease for which a Phase 3 trial is anticipated to start in 2026; under the terms of the acquisition agreement, Viatris has made an upfront payment to Aculys Pharma shareholders as consideration for the acquisition, with additional consideration contingent upon the achievement of specified regulatory and commercial milestones, and royalties on net sales; positive Pivotal study results in Japanese patients were recently achieved in both narcolepsy and OSAS; in the narcolepsy Phase 3 trial, the primary endpoint of improvement in excessive daytime sleepiness (EDS) compared to a placebo group using the Epworth Sleepiness Scale (ESS) was met; statistically significant difference in ESS was observed between the two groups; furthermore, the key secondary endpoint of the frequency of cataplexy attacks showed a suppression effect comparable to that observed in prior global Phase 3 trials; no serious adverse events were noted, and the safety and tolerability results were consistent with global clinical trials; the OSAS Phase 3 trial evaluated the effect of pitolisant in Japanese patients with OSAS who were experiencing residual EDS despite treatment with CPAP therapy; at the end of the 12-week treatment period, patients receiving pitolisant scored lower on the ESS used to measure EDS compared to those in the placebo group and this difference was statistically significant (p=0.007); additionally, safety and tolerability were consistent with results from global clinical studies. Because forward-looking statements inherently involve risks and uncertainties, actual future results may differ materially from those expressed or implied by such forward-looking statements. Factors that could cause or contribute to such differences include, but are not limited to: Viatris not realizing the anticipated benefits of the acquisition; the uncertainties inherent in research and development, including the outcomes of clinical trials; the ability to meet anticipated clinical endpoints; the possibility of unfavorable new clinical data and further analyses of existing clinical data; the risk that clinical trial data are subject to differing interpretations and assessments by regulatory authorities; whether regulatory authorities will be satisfied with the design of and results from clinical studies; actions and decisions of healthcare and pharmaceutical regulators; our ability to comply with applicable laws and regulations; changes in healthcare and pharmaceutical laws and regulations in the U.S. and abroad; any regulatory, legal or other impediments to Viatris’ ability to bring new products to market; products in development and/or that receive regulatory approval may not achieve expected levels of market acceptance, efficacy or safety; longer review, response and approval times as a result of evolving regulatory priorities and reductions in personnel at health agencies; Viatris’ or its partners’ ability to develop, manufacture, and commercialize products; the scope, timing and outcome of any ongoing legal proceedings, and the impact of any such proceedings on Viatris; Viatris’ failure to achieve expected or targeted future financial and operating performance and results; goodwill or impairment charges or other losses; any changes in or difficulties with Viatris’ manufacturing facilities; risks associated with international operations; changes in third-party relationships; the effect of any changes in Viatris’ or its partners’ customer and supplier relationships and customer purchasing patterns; the impacts of competition; changes in the economic and financial conditions of Viatris or its partners; uncertainties regarding future demand, pricing and reimbursement for Viatris’ products; uncertainties and matters beyond the control of management, including but not limited to general political and economic conditions, potential adverse impacts from future tariffs and trade restrictions, inflation rates and global exchange rates; and the other risks described in Viatris’ filings with the Securities and Exchange Commission (“SEC”). Viatris routinely uses its website as a means of disclosing material information to the public in a broad, non-exclusionary manner for purposes of the SEC’s Regulation Fair Disclosure (Reg FD). Viatris undertakes no obligation to update these statements for revisions or changes after the date of this press release other than as required by law.

 

SOURCE Viatris Inc.

ROSWELL, Ga., Oct. 15, 2025 /PRNewswire/ — Sebela Women’s Health Inc., a part of Sebela Pharmaceuticals, today announced that MIUDELLA^® Hormone-Free Copper Intrauterine System (IUS) has been named to TIME’s list of the best inventions of 2025.

To compile this year’s list, TIME solicited nominations from TIME editors and correspondents around the world through an online application process, paying special attention to growing fields—such as health care and AI. TIME then evaluated each contender on a number of key factors, including originality, efficacy, ambition, and impact. See the full list here: time.com/collections/best-inventions-2025/ and MIUDELLA^® brief here: https://time.com/collections/best-inventions-2025/7318454/sebela-miudella/. 

MIUDELLA^® is the first hormone-free copper IUD in the U.S. in over 40 years. It was approved on February 24, 2025, by the U.S. Food and Drug Administration for the prevention of pregnancy in females of reproductive potential for up to three years, and it is expected to be available to patients through trained healthcare providers in the U.S. in the first half of 2026.

“Sebela Women’s Health is delighted that MIUDELLA was named to TIME’s Best Inventions of 2025 list,” said Kelly Culwell, MD, Head of Research and Development, Sebela Women’s Health. “This distinction further supports our belief that the novel design of MIUDELLA will offer an innovative option for birth control for women nationwide.”

Guidelines from the American College of Obstetrics and Gynecology state that long-acting reversible contraceptive (LARC) methods, including intrauterine devices and contraceptive implants, are the most effective contraceptive methods, have few contraindications, and are appropriate for almost all patients.¹ While there are a variety of contraceptive methods available to women, 41.6 percent of pregnancies in the U.S. are unintended.²

INDICATION FOR MIUDELLA^®
MIUDELLA^® is a copper-containing intrauterine system (IUS) indicated for prevention of pregnancy in females of reproductive potential for up to 3 years.

IMPORTANT SAFETY INFORMATION

• WARNING: Improper insertion of intrauterine systems, including MIUDELLA^®, increases the risk of complications.
• Proper training prior to first use of MIUDELLA^® can minimize the risk of improper insertion.
• MIUDELLA^® is available only through a restricted program under a Risk Evaluation and Mitigation Strategy (REMS) called the MIUDELLA^® REMS program to ensure healthcare providers are trained on the proper insertion of MIUDELLA^® prior to first use. Further information is available at miudellarems.com and 1-855-337-0772.
• Contraindications: Don’t use MIUDELLA^® if you are or may be pregnant, have a uterine anomaly that may affect correct placement, acute pelvic inflammatory disease, postpartum endometritis or postabortal endometritis in past 3 months, known or suspected uterine or cervical malignancy, for use as post-coital contraception (emergency contraception), unexplained bleeding, untreated acute cervicitis or vaginitis or other lower genital tract infection, conditions associated with increased susceptibility to pelvic infections, Wilson’s disease, a previously placed IUS that has not been removed and/or hypersensitivity to any component of MIUDELLA^® including copper, nitinol or any trace elements present in the copper components of MIUDELLA^®.
• Pregnancy with MIUDELLA^® is rare but can be life threatening and cause infertility or loss of pregnancy.
• MIUDELLA^® may attach to or go through the uterus and cause other problems.
• Tell your healthcare provider (HCP) if you develop severe pain or fever shortly after placement, miss a period, have abdominal pain, or if MIUDELLA^® comes out. If it comes out, use backup birth control.
• At first, periods may be altered and result in heavier and longer bleeding with spotting in between.
• Tell your HCP you have MIUDELLA^® before having an MRI or a medical procedure using heat therapy.
• Additional common side effects include painful periods, pelvic discomfort/pain, procedural pain, post procedural bleeding, and pain during sex.
• MIUDELLA^® does not protect against HIV or STDs.

Only you and your HCP can decide if MIUDELLA^® is right for you. Available by prescription only. For additional information or to report suspected adverse reactions, please contact Sebela Women’s Health Inc. at 1-866-246-2133. You are encouraged to report negative side effects of prescription drugs to the FDA at www.fda.gov/medwatch or call 1-800-FDA-1088.

Click here for the Full Prescribing Information for MIUDELLA^®.

About Sebela Pharmaceuticals^®

Sebela Pharmaceuticals is a US pharmaceutical company with a market leading position in gastroenterology and a focus on innovation in women’s health. In addition to MIUDELLA^®, Sebela Women’s Health has another next-generation hormonal IUD for contraception in late-stage clinical development. Braintree Laboratories, Inc., a part of Sebela Pharmaceuticals, is the market leader in colonoscopy screening preparations for over 35 years, having invented, developed and commercialized a broad portfolio of innovative prescription colonoscopy preparations and multiple gastroenterology products. Braintree also has several gastroenterology programs in late-stage clinical development including Tegoprazan which is in phase 3 trials for gastro-esophageal reflux disease (GERD), specifically, erosive esophagitis (EE) and non-erosive reflux disease (NERD).

Sebela Pharmaceuticals has offices/operations in Roswell, GA; Braintree, MA; and Dublin, Ireland. Please visit sebelapharma.com for more information or call 844-732-3521.

MIUDELLA is a registered trademark of Sebela Women’s Health Inc.

Forward Looking Statements

This press release and any statements made for and during any presentation or meeting contain forward- looking statements related to Sebela Women’s Health Inc. under the safe harbor provisions of Section 21E of the Private Securities Litigation Reform Act of 1995, as amended, and are subject to risks and uncertainties that could cause actual results to differ materially from those projected. In some cases, forward-looking statements can be identified by terminology such as “will,” “may,” “should,” “could,” “expects,” “intends,” “plans,” “aims,” “anticipates,” “believes,” “estimates,” “predicts,” “potential,” “continue,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. There are a number of factors that could cause actual events to differ materially from those indicated by such forward-looking statements. These factors include, but are not limited to, the development, launch, introduction and commercial potential of IUDs as described herein; growth and opportunity, including peak sales and the potential demand for these IUDs, as well as their potential impact on applicable markets; market size; substantial competition; our ability to continue as a growing concern; our need for additional financing; uncertainties of patent protection and litigation; uncertainties of government or third-party payer reimbursement; dependence upon third parties supply and manufacturing uncertainties; our financial performance and results, including the risk that we are unable to manage our operating expenses or cash use for operations, or are unable to commercialize our products, within the guided ranges or otherwise as expected; and risks related to failure to obtain FDA clearances or approvals and noncompliance with FDA regulations. As with any pharmaceutical under development, there are significant risks in the development, regulatory approval and commercialization of new products. While the list of factors presented here is considered representative, no such list should be considered to be a complete statement of all potential risks and uncertainties. Unlisted factors may present significant additional obstacles to the realization of forward-looking statements. Forward-looking statements included herein are made as of the date hereof, are based on current expectations, and Sebela Women’s Health Inc. does not undertake any obligation to update publicly such statements to reflect subsequent events or circumstances except as required by law.

Contact
Sebela Women’s Health
Erinn White
[email protected]
917-769-2785

¹ ACOG, Clinical Practice Bulletin #186, Nov. 2017 reaffirmed 2021; Committee Statement #5, April 2023. Accessed on April 18, 2023: https://www.acog.org/clinical/clinical-guidance/practice-bulletin/articles/2017/11/long-acting-reversible-contraception-implants-and-intrauterine-devices and https://www.acog.org/clinical/clinical-guidance/committee-statement/articles/2023/03/increasing-access-to-intrauterine-devices-and-contraceptive-implants 
² Centers for Disease Control and Prevention. Accessed on Feb. 18, 2025. https://www.cdc.gov/reproductive-health/hcp/unintended-pregnancy/index.html#:~:text=Overview,2010%20to%2035.7%20in%202019

SOURCE Sebela Pharmaceuticals Inc

Q3 diluted earnings per share of $2.90; Q3 adjusted diluted earnings per share¹ of $2.78, above the top end of guidance; Q4 adjusted diluted earnings per share guidance of $3.00 to $3.50²

Customer investments this year are on track to total over $1 billion focused on improving the experience, product and service, delivering more value for every customer on every flight, and United expects to invest over $1 billion more in 2026

Growing base of brand-loyal customers boosted resilience through macro volatility during the first three quarters of the year and is poised to fuel a strong Q4 as the demand environment strengthens with an expected meaningful improvement in unit revenue year-over-year compared to Q3

Operational excellence continues to lay a solid foundation for the overall business as United flew its largest summer mainline schedule ever in 2025 and had its lowest third-quarter cancel rate in its history³

CHICAGO, Oct. 15, 2025 /PRNewswire/ — United Airlines (UAL) today reported a third-quarter profit ahead of Wall Street expectations. The company had third-quarter pre-tax earnings of $1.3 billion, with a pre-tax margin of 8.2% and adjusted pre-tax earnings¹ of $1.2 billion, with an adjusted pre-tax margin¹ of 8.0%. The company also achieved diluted earnings per share of $2.90 and adjusted diluted earnings per share¹ of $2.78, compared to guidance of $2.25 to $2.75. Total operating revenue grew 2.6% year-over year to $15.2 billion.

These financial results show how the airline has thrived in an economically volatile year thanks to brand-loyal customers who choose to fly United because of the value in the United experience. United continued to benefit from diverse revenue sources during the quarter. In the third quarter, premium cabin revenue rose 6% year-over-year; revenue from Basic Economy rose 4% year-over-year; cargo revenue rose 3% year-over-year and loyalty revenue rose 9% year-over-year. This great momentum has continued so far in the fourth quarter and we expect the fourth quarter of 2025 to have the highest total operating revenue for a single quarter in company history.

“We’ve invested in customers at every price point: Seatback screens, an industry-leading mobile app, extra legroom, a lie-flat United Polaris seat, and fast, free, reliable Starlink on every plane by 2027. Our customers value the United experience, making them increasingly loyal to United,” CEO Scott Kirby said. “Those investments over almost a decade, combined with great service from our people, have allowed United to win and retain brand-loyal customers, leading to economic resilience even with macro economic volatility through the first three quarters of the year and significant upside as the economy and demand are improving in the fourth quarter.”

United continues to make significant investments in winning brand-loyal customers, including more than $1 billion planned on enhancements including Starlink installations, seatback screens, and 25% more on food. Over half of the United narrowbody fleet now has its signature interior and seatback screens, leading to a 15-point increase in customer satisfaction with the inflight entertainment system since third quarter 2022. United plans to invest an additional $1 billion in the customer experience in 2026.

United’s reliable operation is also benefiting customers and building brand loyalty. United had its highest third-quarter completion factor³, carried more than 48 million customers, the most-ever during a quarter, and flew its largest daily mainline schedule with 2,940 daily flights carrying more than 427,000 passengers a day. Six of United’s seven hubs ranked first or second for on-time departures. Reliability continues to be a focus — customers who arrive on time are more than three times as likely to recommend United as compared to customers whose flights are delayed.

United’s network strength is another reason it is winning customer preference. Last week it announced summer 2026 flights to Split, Croatia; Glasgow, Scotland; Santiago de Compostela, Spain; and Bari, Italy, while also bringing back all six new Atlantic destinations from its summer 2025 international expansion. United is the largest carrier across the Atlantic, with service to 46 cities planned for 2026.

“Customers are increasingly choosing an airline that can deliver value for them across the full travel experience, from Basic Economy to United Polaris,” Kirby said. “We are well-positioned to be the airline those brand-loyal customers choose to fly them across the U.S. and around the world.”

Third-Quarter Financial Results

• Capacity up 7.2% compared to third-quarter 2024.
• Total operating revenue of $15.2 billion, up 2.6% compared to third-quarter 2024.
• TRASM down (4.3%) compared to third-quarter 2024.
• CASM down (2.8%), and CASM-ex¹ down (0.9%), compared to third-quarter 2024; 1 point of expense moved from third-quarter 2025 to fourth-quarter 2025 primarily driven by maintenance and a reduction of 1 point of labor expense due to the timing of certain union contracts.
• Pre-tax earnings of $1.3 billion, with a pre-tax margin of 8.2%; adjusted pre-tax earnings¹ of $1.2 billion, with an adjusted pre-tax margin¹ of 8.0%.
• Net income of $0.9 billion; adjusted net income¹ of $0.9 billion.
• Diluted earnings per share of $2.90; adjusted diluted earnings per share¹ of $2.78.
• Average fuel price per gallon of $2.43.
• Ending available liquidity⁴ of $16.3 billion.
• Total debt, finance lease obligations and other financial liabilities of $25.4 billion at quarter end.
• Prepaid the remaining $1.5 billion balance of the MileagePlus bonds, resulting in full repayment of all debt secured by the MileagePlus business.
• Trailing twelve months net leverage⁵ of 2.1x.
• Repurchased $19 million of shares in the third quarter 2025, and have repurchased approximately $612 million of shares year-to-date as of September 30, 2025.

Key Highlights

• Operated the largest daily mainline schedule flown in a quarter, carrying over 48 million customers, a daily average of 427,000 mainline customers on 2,940 daily mainline flights. This included flying the largest international schedule in United’s history, with more than 400 roundtrips per day to a total of 142 destinations.
• Launched a collaboration with Apple TV, bringing the streaming service’s most popular series to customers for free on 130,000+ seatback screens.
• United has updated more than half of its narrowbody fleet with its signature interior and seatback screens, leading to a 15-point increase in customer satisfaction with the inflight entertainment system since third quarter 2022.
• Achieved the highest customer satisfaction rate for a third quarter since 2022 across key customer experience drivers, having invested $85 million in the food and beverage program with another $45 million allotted next year.
• Announced the airline will resume flights to Tel Aviv from Chicago O’Hare and Washington Dulles for the first time since 2023, with flights scheduled to begin in early November.
• Received FAA certification on the airline’s first mainline Starlink-equipped aircraft, operating the first Starlink-equipped commercial flight earlier today. Access to Starlink will be free for all MileagePlus customers and includes game-changing inflight entertainment experiences like streaming services, shopping, gaming and more. Membership to MileagePlus is also free and people can sign-up now at united.com/starlink.

Customer Experience

• Connection Saver saved 290,000 potential missed customer connections, the highest for a third quarter in the company’s history.
• United launched TSA PreCheck Touchless ID at its Denver and Newark hubs, bringing a more seamless customer experience to 12 airports total.
• With a $9 million investment in digital check-in and curbside processes over the last year, nearly half of passengers flown in the quarter bypassed the lobby for a more efficient travel experience, with 85% of customers using digital check-in and the airline achieving the highest ever curbside utilization for a quarter.
• Opened the airline’s fourth United Club location at its Denver hub, a two-story, 33,000 square-foot space for customers to relax and recharge during the travel journey.
• Announced the opening of the United Globe Club at Capital One Arena this fall in collaboration with Monumental Sports and Entertainment, a 24,000 square-foot lounge that offers spectators a VIP experience.

Operations

• United’s Newark hub achieved its best operational performance for a summer in the airline’s history³, and the FAA finalized its order capping flight operations to 72 operations per hour through October of 2026.
• Began operating out of four additional gates at Chicago O’Hare in October, with a fifth gate expected to begin operating later in October. The gates were assigned to United by the City of Chicago and Chicago Department of Aviation’s redetermination process which is based on operations at the airport.
• Achieved the airline’s highest third-quarter completion rate in company history³.
• United Express achieved 43 days with a 100% completion rate, setting a company record for the most days of perfect completion for the year to date at 92³.
• Broke ground on a new, state of the art catering facility at the airline’s Houston hub, that will use automated technology to streamline meal assembly and improve preparation time.

Network

• Announced additional flights to 15 U.S. cities in United’s winter schedule, including two new routes from Newark to Columbia, South Carolina and Chattanooga, Tennessee and increased daily flying to popular warm-weather destinations like Orlando, Florida; Ft. Lauderdale, Florida and Las Vegas, Nevada.
• United expanded both its international and domestic networks, including Denver to Columbia, Missouri; Watertown, South Dakota; and Pierre, South Dakota; Chicago to Columbia, Missouri and Lafayette, Indiana; and Tokyo-Narita to Kaohsiung.
• Added 24 nonstop flights to fly college football fans to games this fall.
• Announced the launch of a codeshare program with ITA Airways, giving customers access to booking one-way tickets to more Italian destinations.

Employees, Communities and Investments

• In the company’s fifth annual September of Service, United partnered with Rise Against Hunger to host more than 20 meal packaging events throughout the system, with more than 3,500 employees volunteering over 11,000 hours throughout the month to package more than 497,000 meals.
• United supported the transport of over 117 responders to recovery efforts at 11 disaster relief events in partnership with Airlink, transporting over 221,000 pounds of cargo in support of 25 non-profit organizations to aid over 644,000 around the world. In total, United transported the most cargo for a third quarter in company history, at 9.5 million pounds of medical shipments and 262,000 pounds of military shipments.
• Committed $250,000 in funding to 24 schools impacted by California wildfires earlier this year.
• Hosted 18 events in honor of Girls in Aviation Day to educate young girls on future possibilities in aviation.
• Strengthened United’s early career pipeline, launching a Campus Ambassador Program at select universities this fall to recruit future talent.
• United Airlines Ventures announced its investment in supersonic aircraft startup Astro Mechanica.

Awards

• United was recognized as a leading employer in Newsweek’s 2026 list of America’s Most Admired Workplaces, America’s Greatest Workplaces for Parents and Families and America’s Greatest Companies, and Forbes’ 2025 list of Best Employers for Women.
• Chief Executive Officer Scott Kirby received APEX International’s Lifetime Achievement award for his continued leadership in reimagining the customer journey.
• United Chief Financial Officer Mike Leskinen was named a Notable Leader in Finance by Crain’s Chicago Business for his strategic foresight and transformative impact on the industry.

Earnings Call

UAL will hold a conference call to discuss third-quarter 2025 financial results, as well as its financial and operational outlook for the fourth-quarter 2025 and beyond, on Thursday, October 16, 2025 at 9:30 a.m. CST/10:30 a.m. EST. A live, listen-only webcast of the conference call will be available at ir.united.com. The webcast will be available for replay within 24 hours of the conference call and then archived on the website.

Outlook

This press release should be read in conjunction with the company’s Investor Update issued in connection with this quarterly earnings announcement, which provides additional information on the company’s business outlook (including certain financial and operational guidance) and is furnished with this press release to the U.S. Securities and Exchange Commission on a Current Report on Form 8-K. The Investor Update is also available at ir.united.com. Management will also discuss certain business outlook items, including providing certain fourth quarter and full year 2025 financial targets, during the quarterly earnings conference call.

The company’s business outlook is subject to risks and uncertainties applicable to all forward-looking statements as described elsewhere in this press release. Please see the section entitled “Cautionary Statement Regarding Forward-Looking Statements.”

About United

At United, Good Leads The Way. With hubs in Chicago, Denver, Houston, Los Angeles, New York/Newark, San Francisco and Washington, D.C., United operates the most comprehensive global route network among North American carriers, and is now the largest airline in the world. For more about how to join the United team, please visit www.united.com/careers and more information about the company is at www.united.com. United Airlines Holdings, Inc., the parent company of United Airlines, Inc., is traded on the Nasdaq under the symbol “UAL”.

Website and Social Media Information

We routinely post important news and information regarding United on our corporate website, www.united.com, and our investor relations website, ir.united.com. We use our investor relations website as a primary channel for disclosing key information to our investors, including the timing of future investor conferences and earnings calls, press releases and other information about financial performance (including financial guidance), reports filed or furnished with the U.S. Securities and Exchange Commission, information on corporate governance and details related to our annual meeting of shareholders. We may use our investor relations website as a means of disclosing material, non-public information (including financial guidance) and for complying with our disclosure obligations under Regulation FD. We encourage investors, the media and others interested in the company to visit this website from time to time, as information is updated and new information is posted. We may also use social media channels to communicate with our investors and the public about our company and other matters, and those communications could be deemed to be material information. Our executive officers may also use certain social media channels, such as X and LinkedIn, to communicate information about earnings results and company updates, which may be of interest to our investors or could be deemed to be material information. The information contained on, or that may be accessed through, our website or social media channels are not incorporated by reference into, and are not a part of, this document.

Cautionary Statement Regarding Forward-Looking Statements:
This press release and the related attachments and Investor Update (as well as the oral statements made with respect to information contained in this release and the attachments) contain certain “forward-looking statements,” within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, relating to, among other things, goals, plans and projections regarding the company’s financial position, results of operations, capital allocation and investments, market position, airline capacity, fleet plan strategy, fares, announced routes (which may be subject to government approval), booking trends, product development, corporate citizenship-related strategy initiatives and business strategy. Such forward-looking statements are based on historical performance and current expectations, estimates, forecasts and projections about the company’s future financial results, goals, plans, commitments, strategies and objectives and involve inherent risks, assumptions and uncertainties, known or unknown, including internal or external factors that could delay, divert or change any of them, that are difficult to predict, may be beyond the company’s control and could cause the company’s future financial results, goals, plans, commitments, strategies and objectives to differ materially from those expressed in, or implied by, the statements. Words such as “should,” “could,” “would,” “will,” “may,” “expects,” “plans,” “intends,” “anticipates,” “indicates,” “remains,” “believes,” “estimates,” “projects,” “forecast,” “guidance,” “outlook,” “goals,” “targets,” “pledge,” “confident,” “optimistic,” “dedicated,” “positioned,” “on track”, “path” and other words and terms of similar meaning and expression are intended to identify forward-looking statements, although not all forward-looking statements contain such terms. All statements, other than those that relate solely to historical facts, are forward-looking statements.

Additionally, forward-looking statements include conditional statements and statements that identify uncertainties or trends, discuss the possible future effects of known trends or uncertainties, or that indicate that the future effects of known trends or uncertainties cannot be predicted, guaranteed or assured. All forward-looking statements in this release are based upon information available to us on the date of this release. We undertake no obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future events, changed circumstances or otherwise, except as required by applicable law or regulation.

Our actual results could differ materially from these forward-looking statements due to numerous factors including, without limitation, the following: execution risks associated with our strategic operating plan; changes in our fleet and network strategy or other factors outside our control resulting in less economic aircraft orders, costs related to modification or termination of aircraft orders or entry into aircraft orders on less favorable terms, as well as any inability to accept or integrate new aircraft into our fleet as planned, including as a result of any mandatory groundings of aircraft; any failure to effectively manage, and receive anticipated benefits and returns from, acquisitions, divestitures, investments, joint ventures and other portfolio actions, or related exposures to unknown liabilities or other issues or underperformance as compared to our expectations; adverse publicity, increased regulatory scrutiny, harm to our brand, reduced travel demand, potential tort liability and operational restrictions as a result of an accident, catastrophe or incident involving us, our regional carriers, our codeshare partners or another airline; the highly competitive nature of the global airline industry and susceptibility of the industry to price discounting and changes in capacity, including as a result of alliances, joint business arrangements or other consolidations; our reliance on a limited number of suppliers to source a majority of our aircraft, engines and certain parts, and the impact of any failure to obtain timely deliveries, additional equipment or support from any of these suppliers; disruptions to our regional network and United Express flights provided by third-party regional carriers; unfavorable economic and political conditions in the United States and globally; reliance on third-party service providers and the impact of any significant failure of these parties to perform as expected, or interruptions in our relationships with these providers or their provision of services; extended interruptions or disruptions in service at major airports where we operate and space, facility and infrastructure constraints at our hubs or other airports (including as a result of government shutdowns); geopolitical conflict, terrorist attacks or security events (including the suspension of our overflying in Russian airspace as a result of the Russia-Ukraine military conflict and interruptions of our flying as a result of the military conflicts in the Middle East, as well as any escalation of the broader economic consequences of any conflicts beyond their current scope or a delay in any planned resumption of service to area impacted by conflict); any damage to our reputation or brand image; our reliance on technology and automated systems to operate our business and the impact of any significant failure or disruption of, or failure to effectively integrate and implement, these technologies or systems; increasing privacy, data security and cybersecurity obligations or a significant data breach; increased use of social media platforms by us, our employees and others; the impacts of union disputes, employee strikes or slowdowns, and other labor-related disruptions or regulatory compliance costs on our operations or financial performance; any failure to attract, train or retain skilled personnel, including our senior management team or other key employees; the monetary and operational costs of compliance with extensive government regulation of the airline industry; current or future litigation and regulatory actions, or failure to comply with the terms of any settlement, order or agreement relating to these actions; costs, liabilities and risks associated with environmental regulation and climate change; high and/or volatile fuel prices or significant disruptions in the supply of aircraft fuel; the impacts of our significant amount of financial leverage from fixed obligations and the impacts of insufficient liquidity on our financial condition and business; failure to comply with financial and other covenants governing our debt; limitations on our ability to use our net operating loss carryforwards and certain other tax attributes to offset future taxable income for U.S. federal income tax purposes; our failure to realize the full value of our intangible assets or our long-lived assets, causing us to record impairments; fluctuations in the price of our common stock; the impacts of seasonality, and other factors associated with the airline industry; increases in insurance costs or inadequate insurance coverage; risks relating to our repurchase program for shares of common stock and certain warrants exercisable for common stock; and other risks and uncertainties set forth in Part I, Item 1A. Risk Factors of our Annual Report on Form 10-K for the fiscal year ended December 31, 2024 and under “Economic and Market Factors” and “Governmental Actions” in Part I, Item 2. Management’s Discussion and Analysis of Financial Condition and Results of Operations, of our Quarterly Report on Form 10-Q for the quarter ended June 30, 2025, as well as other risks and uncertainties set forth from time to time in the reports we file with the U.S. Securities and Exchange Commission.

Non-GAAP Financial Information:
In discussing financial results and guidance, the company refers to financial measures that are not in accordance with U.S. Generally Accepted Accounting Principles (“GAAP”). The non-GAAP financial measures are provided as supplemental information to the financial measures presented in this press release that are calculated and presented in accordance with GAAP and are presented because management believes that they supplement or enhance management’s, analysts’ and investors’ overall understanding of the company’s underlying financial performance and trends and facilitate comparisons among current, past and future periods. Non-GAAP financial measures typically have exclusions or adjustments that include one or more of the following characteristics, such as being highly variable, difficult to project, unusual in nature, significant to the results of a particular period or not indicative of past or future operating results. These items are excluded because the company believes they neither relate to the ordinary course of the company’s business nor reflect the company’s underlying business performance.

Because the non-GAAP financial measures are not calculated in accordance with GAAP, they should not be considered superior to and are not intended to be considered in isolation or as a substitute for the related GAAP financial measures presented in the press release and may not be the same as or comparable to similarly titled measures presented by other companies due to possible differences in method and in the items being adjusted. We encourage investors to review our financial statements and publicly-filed reports in their entirety and not to rely on any single financial measure. The company does not provide a reconciliation of forward-looking measures where the company believes such a reconciliation would imply a degree of precision and certainty that could be confusing to investors and is unable to reasonably predict certain items contained in the GAAP measures without unreasonable efforts. This is due to the inherent difficulty of forecasting the timing or amount of various items that have not yet occurred and are out of the company’s control or cannot be reasonably predicted. For the same reasons, the company is unable to address the probable significance of the unavailable information. Forward-looking non-GAAP financial measures provided without the most directly comparable GAAP financial measures may vary materially from the corresponding GAAP financial measures.

Please refer to the tables accompanying this release for a description of the non-GAAP adjustments and reconciliations of the historical non-GAAP financial measures used to the most comparable GAAP financial measure and related disclosures.

Change in Presentation:
In the first quarter of 2025, the Company changed its rounding presentation to the nearest whole number in millions of reported amounts, except per share data or as otherwise designated. As such, certain columns and rows within the financial statements and tables presented may not sum due to rounding. Per unit amounts have been calculated from the underlying whole-dollar amounts. This change is not material and does not impact the comparability of our condensed consolidated financial statements.

-tables attached-

UNITED AIRLINES HOLDINGS, INC.

¹ NON-GAAP FINANCIAL INFORMATION

UAL evaluates its financial performance utilizing various accounting principles generally accepted in the United States of America (GAAP) and non-GAAP financial measures. The non-GAAP financial measures are provided as supplemental information to the financial measures presented in this press release that are calculated and presented in accordance with GAAP and are presented because management believes that they supplement or enhance management’s, analysts’ and investors’ overall understanding of the company’s underlying financial performance and trends and facilitate comparisons among current, past and future periods.

Because the non-GAAP financial measures are not calculated in accordance with GAAP, they should not be considered superior to and are not intended to be considered in isolation or as a substitute for the related GAAP financial measures presented in the press release and may not be the same as or comparable to similarly titled measures presented by other companies due to possible differences in method and in the items being adjusted. We encourage investors to review our financial statements and publicly-filed reports in their entirety and not to rely on any single financial measure.

The information below provides an explanation of certain adjustments reflected in the non-GAAP financial measures and shows a reconciliation of non-GAAP financial measures reported in this press release to the most directly comparable GAAP financial measures. Within the financial tables presented, certain columns and rows may not add due to the use of rounded numbers. Percentages, ratios and earnings per share amounts presented are calculated from the underlying amounts.

CASM-ex: CASM is a common metric used in the airline industry to measure an airline’s cost structure and efficiency. UAL reports CASM excluding special charges, third-party business expenses, fuel expense, and profit sharing. UAL believes that adjusting for special charges is useful to investors because those items are not indicative of UAL’s ongoing performance. UAL also believes that excluding third-party business expenses, such as maintenance, flight academy, ground handling and catering services for third parties, provides more meaningful disclosure because these expenses are not directly related to UAL’s core business. UAL also believes that excluding fuel expense from certain measures is useful to investors because it provides an additional measure of management’s performance excluding the effects of a significant cost item over which management has limited influence. UAL excludes profit sharing because it believes that this exclusion allows investors to better understand and analyze UAL’s operating cost performance and provides a more meaningful comparison of our core operating costs to the airline industry.

Adjusted EBITDA and Adjusted EBITDAR: We calculate Adjusted EBITDA by adding interest, taxes, depreciation and amortization to net income and adjusting for special charges, nonoperating unrealized (gains) losses on investments, net and nonoperating debt extinguishment and modification fees. UAL believes that adjusting for these items is useful to investors because they are not indicative of UAL’s ongoing performance. Effective January 1, 2025, Adjusted EBITDA is further adjusted by the fixed portion of operating lease expense, instead of solely aircraft rent as was the case historically, to calculate Adjusted EBITDAR. We believe this change provides investors with enhanced comparability to peers and better reflects our performance. The change in EBITDAR calculation methodology does not represent a change in management’s expectations. Prior period amounts have been recast to conform to the current period presentation.

Adjusted Capital Expenditures: UAL believes that adjusting capital expenditures for assets acquired through the issuance or modification of debt, finance leases and other financial liabilities is useful to investors in order to appropriately reflect the total amounts spent on capital expenditures.

Free Cash Flow: Effective January 1, 2025, we define free cash flow as the sum of net cash from operating activities and net cash from investing activities, adjusted for the net change in short-term investments and the net change in restricted cash. We believe adjusting for short-term investments and restricted cash activity provides investors a better understanding of the company’s free cash flow generated by our core operations. The change in free cash flow calculation methodology does not represent a change in management’s expectations. We believe this change provides investors with enhanced comparability to peers and better reflects our performance. Prior period amounts have been recast to conform to the current period presentation.

Free Cash Conversion: Free cash conversion is a non-GAAP financial measure that is equal to free cash flow divided by adjusted net income. UAL provides free cash conversion because it provides a better understanding of the company’s free cash flow generated by our core operations relative to our profitability. We are not providing a target for or a reconciliation to net cash provided by operating activities or net income, the most directly comparable GAAP measures, because we are unable to predict the excluded items contained in the GAAP measure without unreasonable efforts, and therefore we also are not able to determine the probable significance of such items.

Adjusted Total Debt and Adjusted Net Debt: Adjusted total debt is a non-GAAP financial measure that includes current and long-term debt, finance lease obligations and other financial liabilities, current and noncurrent operating lease obligations and noncurrent pension and postretirement obligations. Adjusted net debt is adjusted total debt minus cash, cash equivalents and short-term investments. UAL provides adjusted total debt and adjusted net debt because we believe these measures provide useful supplemental information for assessing the company’s debt and debt-like obligation profile. 

Net Leverage: Net leverage is a non-GAAP financial measure that is equal to adjusted net debt divided by trailing twelve month Adjusted EBITDAR. UAL provides net leverage because we believe it provides useful supplemental information for assessing the company’s debt level. See the above descriptions of Adjusted Net Debt and Adjusted EBITDAR.

Operating and nonoperating special charges (credits) and unrealized losses on investments included the following:

During the three and nine months ended September 30, 2025, the company recorded $73 million and $295 million, respectively, of net gains on sale of assets and other special charges, which were primarily comprised of $75 million and $336 million, respectively, of gains on various aircraft sale-leaseback transactions.

During the nine months ended September 30, 2025, the company also recorded a $561 million special charge in connection with a labor contract ratification bonus for the company’s employees represented by the Association of Flight Attendants.

Effective tax rate:

The company’s effective tax rates were as follows:

The provision for income taxes is based on the estimated annual effective tax rate, which represents a blend of federal, state and foreign taxes and includes the impact of certain nondeductible items. The decrease in the effective tax rate in the three and nine months ended September 30, 2025, as compared to the same period in 2024, was primarily due to a release of valuation allowance related to realized capital gains.

SOURCE United Airlines