A Smart Exoskeleton Glove Helps People With Hand Paralysis Grasp Everyday Objects
Key Takeaways:
- Researchers in Munich have built a soft, air-powered glove that helps people with hand paralysis grasp everyday objects again.
- Electrical signals from the forearm muscles, read by machine learning, predict a person’s intention to grasp with up to 97% reliability.
- A man living with ALS used the glove to hold a fork for the first time in four years.
A soft exoskeleton driven by air and intention
A soft, pneumatic glove developed at the Technical University of Munich (TUM) is giving people with hand paralysis a way to grasp objects once more. The device was created by researchers at the TUM Chair of Cognitive Systems, who use electrical signals from the forearm muscles to reliably predict the moment a person intends to reach for something. Its designers believe it could one day support people whose hands have been paralysed as a result of accidents or neurological disorders. The research is published in the journal Nature Machine Intelligence.
The team calls the device a “soft-hand exoskeleton”. At its heart is a fabric glove, developed by the researchers, with air cushions fitted to its outer surface. Those cushions are inflated through a total of 13 tubes, each one providing targeted support for the specific hand movements needed to hold a plate or grasp a glass, fork or spoon. Because the cushions can be inflated independently, every finger can be bent and straightened on its own, and the wrist can be rotated too, so that an object can be held securely in the hand.
Reading the intention to grasp
The clever part is knowing when the wearer actually wants to grasp something. To work this out, the researchers measure muscle activity in the forearm. Sensors placed on the forearm capture the faint electrical signals produced by the muscles, and machine learning then analyses those signals to determine the intended movement.
Keeping hold of an object once it has been picked up is a separate challenge. “To prevent objects from being dropped accidentally, we use additional motion sensors to detect transport movements and keep the exoskeleton’s grip securely closed throughout the movement,” says researcher Nicolas Berberich.
Devices like the soft-hand exoskeleton reflect a wider movement of artificial intelligence into everyday clinical care, and professional bodies have increasingly urged healthcare professionals to build the judgement needed to use such tools safely and effectively – the focus of the CPD-accredited digital health training now offered by providers including the College of Contemporary Health.
A soft-hand exoskeleton that anyone can afford
For the team, the appeal of the glove lies as much in its simplicity as in its sophistication. “Our solution is intelligent in two ways,” explains Dr John Nassour. “On the one hand, we’ve developed a highly reliable method of predicting grasping movements by inferring intentions from signals with 97% reliability. On the other hand, with our glove, we’ve developed hardware that optimally supports the intended movements.”
There is a practical advantage on top of that. Dr Nassour sewed the glove himself, and the fabric it requires costs very little. It may not look high-tech at first glance, but it can be used by many people living with paralysis. “We’ve found a solution that anyone can afford but still works very well,” says Prof. Gordon Cheng, director of the Institute for Cognitive Systems.
Central to the project was close collaboration with a man living with amyotrophic lateral sclerosis (ALS).
Working with a person living with ALS
People with ALS gradually lose control of their movements. This happens because the nerve cells responsible for contracting skeletal muscle become damaged and continue to degenerate over time.
By the start of the project, the participant already had very little control over his hands, but he could still move the first joint of his thumb. The researchers built their approach around the strongest signals his thumb muscles could still produce. To record this electromyogram, they attached a sensor to his forearm that picks up the strong signals from the flexor pollicis longus muscle as soon as it moves. Those signals, in turn, trigger the inflation of the glove’s air cushions.
Picking up a fork for the first time in four years
Even though the signals were very weak, the system correctly recognised the participant’s intention in 9 out of 10 cases. With the glove’s support, he was able to reach for objects, hold a fork for the first time in four years, and pick up small cubes and drop them into a container.
A video game played its own part in that progress. The participant had to make a character jump using only the movement of his thumb joint, a simple exercise that helped sharpen the system’s response. The researchers found that just five minutes of this practice was enough to greatly improve his ability to grasp objects. “This patient has shown us that our soft-hand exoskeleton can support him despite one of the most severe neurological disorders,” says Prof. Cheng.
Adapting the glove for more people
The team is now looking beyond ALS. “We are now adapting the concept for other patients, such as stroke survivors,” the researcher adds. A central finding of the current study is that people with severe impairments can more effectively regain the ability to grasp objects with the help of the glove.
That potential is echoed by clinicians working alongside the researchers. Neurologist Prof. Tobias Wächter, from the partner institution Klinik Passauer Wolf, is convinced of what the specialised glove could offer. “In principle, this glove can help people with flaccid paralysis, including, for example, people who have sustained peripheral nerve damage following motorcycle or bicycle accidents, or patients with polyneuropathy,” says Prof. Wächter.
CCH insight
As AI-assisted and digital health tools move from the research lab into everyday practice, the ability to evaluate them safely and confidently is fast becoming a core clinical skill. The College of Contemporary Health’s CPD-accredited short course AI Essentials for Primary Care (3.5 CPD hours) helps nurses, pharmacists, physician associates and the wider team assess new tools, work within clinical governance, and use them responsibly in patient care – no technical background required.
Explore AI Essentials for Primary Care →
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Machine Learning Tool Helps Paediatricians Identify Children at Risk of Persistent Asthma
Key Takeaways:
- A machine learning tool that reads data already held in a child’s electronic health record helped paediatricians more accurately judge which young children are at risk of persistent asthma.
- In a pilot randomised trial using standardised clinical cases, clinicians using the tool reached an average accuracy of 83%, compared with 61% for standard assessment alone.
- The tool is designed to support clinical judgement rather than replace it, and requires no additional tests or questionnaires.
Support for a difficult clinical judgement
A machine learning tool that analyses information already captured in a child’s electronic health record (EHR) has helped paediatricians assess asthma risk more accurately in standardised clinical case scenarios, according to a pilot randomised clinical trial led by a researcher at the Regenstrief Institute. The study was published in the journal Scientific Reports.
The trial evaluated a machine learning-enabled clinical decision support tool known as the Passive Digital Marker. The tool draws on routinely collected EHR data to classify young children as having either a high or a low risk of going on to develop persistent asthma.
Why early asthma risk is hard to predict
Asthma is one of the most common long-term conditions of childhood, yet predicting which young children who have wheezing or other respiratory symptoms will later develop persistent asthma remains difficult. Some children outgrow their early symptoms, while others need ongoing treatment. That uncertainty makes early risk assessment an important, but genuinely challenging, part of paediatric care.
“This tool doesn’t replace a pediatrician’s clinical judgment,” said Arthur H. Owora, PhD, Regenstrief Institute research scientist and lead author of the study. “It helps bring together years of clinical information that’s already in the electronic health record, giving clinicians another source of information when making decisions about a child’s asthma risk.”
How the Passive Digital Marker works
Unlike many prediction tools, the Passive Digital Marker requires no extra testing and asks families to complete no additional questionnaires. Instead, it analyses information that has already been documented in the child’s EHR, including respiratory symptoms, allergies, medication history, respiratory infections and family history. It then presents clinicians with a straightforward high-risk or low-risk assessment.
This approach is intended to save clinicians’ time and reduce the burden on families, since it relies on data that has been gathered over the course of a child’s routine care rather than requiring anything new at the point of decision.
What the trial found
Paediatricians using the tool correctly predicted future asthma more often than those relying on standard assessment alone, achieving an average accuracy of 83% compared with 61%. The improvement was largely driven by better identification of children who went on to develop persistent asthma – the group that is most important to recognise early and hardest to spot.
The researchers stress that the tool is meant to support clinical decision-making, not to supplant it. Its value lies in helping clinicians quickly synthesise years of patient information into a single, easy-to-interpret risk assessment that sits alongside their own expertise. That distinction – between having an AI tool to hand and knowing how to weigh what it tells you – is becoming central to how clinicians are expected to work with these systems.
Limitations and next steps
Because the study used standardised patient cases rather than real-world clinical encounters, further research is needed to establish whether the tool improves outcomes for children in everyday paediatric practice. The pilot demonstrates promise in a controlled setting, but real-world validation is the necessary next stage before wider adoption.
CCH insight
Tools like the Passive Digital Marker are only ever as good as a clinician’s ability to judge when to lean on them and when to look again. That skill – evaluating an AI tool, recognising where it can mislead, and putting sensible governance around its use – is exactly what our short course AI Essentials for GPs: Tools, Ethics and Everyday Applications is designed to build. It’s a 3.5-hour, fully online CPD course led by Prof. Mike Bewick and Dr Dipesh Naik. [Explore the course →]
Funding and authorship
The study was supported in part by the National Institutes of Health under grant K01HL166436. In addition to Owora, it was co-authored by Bowen Jiang, M.S., and Yash Shah, M.S., of the Division of Pediatric Pulmonology, Allergy/Immunology and Sleep Medicine, Department of Pediatrics, Riley Hospital for Children, Indiana University School of Medicine.
Source: Regenstrief Institute
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Physicians May Struggle to Spot AI Errors, Even When Evidence Contradicts the Advice
Key Takeaways:
- In experiments involving decisions about hypothetical patients, physicians tended to trust incorrect advice labelled as artificial intelligence (AI) generated, even when they had the chance to notice that patient recovery data contradicted it.
- Across both experiments, physicians rated the AI system as reliable and did not draw on the recovery data to conclude that its recommendations were wrong; in the second experiment, they failed to notice that the treatment was entirely ineffective.
- The findings, published in the open-access journal PLOS Digital Health by Aranzazu Vinas of the University of the Basque Country and colleagues, point to real challenges for the widely held assumption that a human will reliably catch and correct an algorithm’s mistakes.
What the research examined
New research suggests that physicians may find it difficult to learn from experience when that experience runs counter to advice presented as coming from an AI system. In a series of experiments in which physicians made decisions about treating hypothetical patients, they tended to trust incorrect AI-labelled recommendations, even after being given the opportunity to notice that patient recovery data contradicted those recommendations.
The work was carried out by Aranzazu Vinas of the University of the Basque Country, Spain, together with colleagues, and is published in the open-access journal PLOS Digital Health.
Why AI classification matters in care
AI systems can help physicians categorise patients according to their differing care needs, for example by estimating whether a particular patient is more or less likely to benefit from a given treatment. Because these systems are not perfect, they are intended to be used as suggestions rather than instructions, with any potential errors caught and corrected by the physician using them.
This safeguard rests on an assumption that is easy to take for granted: that a human in the loop will notice when the algorithm is wrong. Prior research, however, has shown that people in general struggle to spot and correct mistakes made by AI. Vinas and colleagues set out to explore how far that difficulty extends to physicians in particular.
How the experiments worked
The researchers analysed data from 223 physicians who took part anonymously in online experiments. Participants were asked to imagine that they had the option to treat patients for a rare disease using a treatment that was not yet proven and still under development. They were told that an AI system had identified which patients were more, or less, likely to benefit from that treatment.
The physicians then chose which patients to treat. After being shown data on how those patients recovered, they rated their perceptions of how reliable the AI system was.
The design contained a deliberate mismatch. The actual effectiveness of the hypothetical treatment did not align with the AI’s recommendations. In the first experiment, the treatment was equally, and moderately, effective for all patients. In the second experiment, it was equally ineffective for everyone. In each case, the recovery data available to physicians should, in principle, have allowed them to see that the AI’s classification did not hold up.
What the physicians did
In both experiments, the physicians tended to rate the AI system as reliable, and they did not appear to use the patient recovery data to conclude that the AI’s recommendations were incorrect. In the second experiment, they did not realise that the treatment was entirely ineffective.
As lead author Aranzazu Vinas notes: “In both experiments, physicians mostly trusted the AI’s classifications and had trouble learning from the feedback. Furthermore, in the second experiment, professionals did not notice that the treatment was completely ineffective.”
Co-author Helena Matute adds: “People tend to say that there is always a human controlling the algorithm, but our experiments show that doctors (as well as anyone else) have problems in learning from the available evidence when it contradicts the suggestions of an algorithm.”
What it means for healthcare
Taken together, the results highlight potential challenges for incorporating AI-based classification into healthcare. If the human overseeing an algorithm cannot readily detect its errors, even when contradicting evidence is in front of them, then the reassurance that a clinician will always catch a mistake may be weaker than commonly assumed.
The authors suggest that future research could build on this study, for instance by developing and testing strategies and protocols designed to strengthen human critical thinking and the detection of AI errors. The aim would be to maximise the benefits of human-AI collaboration while minimising the potential for error.
Co-author Fernando Blanco summarises the wider purpose of this line of enquiry: “It is important to investigate the errors that humans (including doctors) make when working with algorithms, in order to learn how to minimize the problems that arise from them.”
Building the habit of questioning AI
While researchers work on formal protocols, individual clinicians can already sharpen how they interrogate AI output. Knowing when to trust a recommendation, and when to challenge it, is a clinical skill rather than a technical one, and it is one that structured training can help build. Our short course AI Essentials for GPs: Tools, Ethics and Everyday Applications introduces practical frameworks, including the SAFER Evaluation Framework, for spotting errors, fabrications, and outdated recommendations before they reach a patient. For clinicians who want a reliable method for the kind of critical checking this study suggests is all too easy to skip, it is a useful place to start.
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NHS App to Use AI to Decide Which Service Suits Patients Best
Key Takeaways:
- New AI triage software on the NHS App will direct patients to the most suitable service, reaching all app users by April 2028.
- An early Sussex trial cut phone queuing for appointments by 29%, part of a £10bn government drive to modernise NHS technology.
- AI note-taking tools will also expand across England, with health bodies urging safeguards on safety, confidentiality and digital inclusion.
A new AI front door to NHS services
NHS England has announced that artificial intelligence software will be used within the NHS App to work out which service is most appropriate for patients in England. The new triage tool asks patients a series of questions and uses their answers to direct them towards the most suitable option, whether that is a GP appointment, a pharmacy, A&E, a community service, or self-care advice.
The health service has described the change as part of a “major overhaul” of its technology. NHS England said the update would reach more than 200,000 patients over the next 12 months and would be available to all NHS App users by April 2028.
The rollout has been largely welcomed. However, some health bodies have urged the NHS to keep patient safety, confidentiality and inclusion front of mind as clinical services grow more reliant on AI.
How the triage tool will work
According to NHS England, the tool will offer advice, suggest services and book appointments. One of its central aims is to reduce the time people spend waiting on the phone – a familiar pressure point when GP surgeries typically open their phone lines at 08:00.
Early trial results and the clinician’s view
An initial trial at Wealden Ridge Medical Partnership in Sussex saw a 29% reduction in the number of people queuing on the phone for an appointment.
Dr Ragu Rajan, who works at the practice, said that integrating the tool “means our patients can tell us what they need, when they need it, and be directed to the right care first time.”
He added that the technology had supported rather than supplanted clinical judgement: “It hasn’t replaced our judgement – it’s given us back the time to use it.”
Ministerial backing and investment
Health Secretary James Murray sought to reassure the public that the rollout did not mean an AI programme would ultimately decide whether patients saw a doctor. Speaking on the BBC’s Sunday with Laura Kuenssberg programme, he said the change meant investment would be put in to “modernise” the NHS and to ensure the benefits were spread “around the country”.
The initiative forms part of a £10bn investment, allocated by the government in 2025, to overhaul the NHS’s technology, digital and data systems.
Sir Jim Mackey, chief executive of NHS England, said the tool would “help get patients to the best service for their needs first time… so that clinicians can make sure those most in need of a GP appointment can get one sooner”.
AI note-taking to expand across England
Alongside the triage tool, there will be an England-wide rollout of AI tools that record conversations between patients and NHS staff in order to generate real-time transcriptions and clinical summaries.
The expansion will begin with hospital appointments that do not require an overnight stay at four NHS trusts in and around London: St George’s, Epsom and St Helier, Croydon, and Kingston and Richmond.
Alder Hey Children’s NHS Foundation Trust in Liverpool and Manchester University NHS Foundation Trust are also expanding their existing AI note-taking programmes.
A trial led by Great Ormond Street Hospital for Children, carried out across nine NHS sites in London, found that NHS staff spent almost 25% more of their time interacting with patients when using the note-taking technology.
Welcome tempered by calls for safeguards
The Royal College of Nursing’s chief nursing officer, Prof Lynn Woolsey, said the rollout could mark “an important step in upgrading technology in the NHS” and could “ease the administrative burden on nursing staff”. She stressed, however, that patient safety and confidentiality must be at the “heart of any AI triage system, with a guarantee that a health professional will be the one making decisions at key points in that process”.
Pritesh Mistry, a fellow at the King’s Fund think-tank, said the announcement “could help turbo-charge improvements in how [the] NHS uses modern technology to deliver better care for patients”.
“People should find it easier to have support at the right time and in a way that best suits them, digitally or physically,” she added. “And this means the NHS will need to keep a strong focus on ensuring that people are not digitally excluded as clinical services become increasingly reliant on technology.”
Political response
Conservative shadow health secretary Stuart Andrew said: “Any innovation that improves patient care and helps the NHS work more effectively should be welcomed.
“But new technology must be introduced with a fully-funded plan that delivers value for taxpayers.”
Source: BBC News
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When the Diagnosis Arrives by App: Why Most Patients Still Want a Human Voice
Key Takeaways:
- In a UT Southwestern survey of more than 2,400 people diagnosed with cancer, 75% said they would prefer to receive the news directly from their physician – in person or by telemedicine – rather than through an electronic patient portal.
- More than half of those who first learned of their diagnosis via the portal were alone at the time, often without support from a clinician or family member.
- Researchers are calling for a more personalised approach, including better portal notification settings, tiered or delayed release of sensitive findings, and plain-language summaries of radiology and pathology reports.
A digital convenience with an emotional cost
Electronic patient portals have transformed how quickly people can see their own test results. For routine bloodwork or a clear scan, near-instant access is a welcome convenience. But the same speed raises a difficult question for clinicians: how should a new cancer diagnosis be communicated when a patient can open the result on a phone before anyone has had the chance to talk it through?
That tension has grown sharper since 2021, when a provision of the 21st Century Cures Act came into force in the United States. The regulation requires that patients have timely, unrestricted access to their electronic health information – which, in practice, means a growing number of people are discovering a new or recurrent cancer diagnosis through their portal, sometimes with no clinician present to interpret it or answer the questions that immediately follow.
What the UT Southwestern survey found
A new survey carried out at UT Southwestern Medical Center suggests that, for most people facing a cancer diagnosis, faster is not better. The findings, published in JAMA Network Open, show that 75% of respondents would prefer to learn about a cancer diagnosis directly from their physician, whether in person or through a telemedicine appointment.
The 2025 survey gathered responses from more than 2,400 people who were diagnosed with cancer at the Harold C. Simmons Comprehensive Cancer Center between 2019 and 2023, giving the researchers a substantial real-world picture of how patients want sensitive results delivered.
According to the study’s lead author, Sheena Bhalla, M.D., Assistant Professor of Internal Medicine in the Division of Hematology and Oncology and a medical oncologist at the Simmons Cancer Center, the broad enthusiasm for digital access does not extend neatly to oncology. “While most patients in the general population appreciate rapid electronic access to test results, the situation for patients with cancer is much more nuanced,” she said. “Learning about a cancer diagnosis without the ability to immediately ask questions or discuss next steps with a trusted clinician can add to the significant stress, uncertainty, and fear that patients experience.”
Preferences are not one-size-fits-all
The survey also revealed that there is no single right way to share a result. Preferences varied according to people’s prior experiences, how frequently they used their portal, and their demographic characteristics. Men, for instance, were more likely than women to prefer learning of a diagnosis through the portal.
For senior author David Gerber, M.D., Professor of Internal Medicine in the Division of Hematology and Oncology and of Epidemiology in the Peter O’Donnell Jr. School of Public Health, and co-Director of the Simmons Cancer Center Office of Education and Training, that variation is precisely the point. “These findings highlight the need for a more personalized, tailored approach to communicating sensitive and life-changing results,” he said. “Moving beyond a one-size-fits-all approach can help clinicians provide a more thoughtful, compassionate patient experience.”
The hidden consequence: facing the news alone
Perhaps the most striking insight concerns the circumstances in which people are receiving these results. Among those who learned of their diagnosis through the portal, more than half reported that they were alone when they read it.
Dr Bhalla described this as one of the most troubling side effects of real-time access. “That’s one of the most unintended consequences of real-time access,” she said. “Patients are often alone without support from their physician or family at one of their most vulnerable moments.”
Possible solutions for clinicians and health systems
The researchers are clear that the answer is not to roll back access, but to design around it more thoughtfully. They point to several potential measures, including raising awareness among both clinicians and patients of the portal notification settings already available; developing tiered or delayed-release approaches for particularly sensitive findings; and integrating supportive digital tools such as plain-language summaries for radiology and pathology reports.
Policy is beginning to catch up. Since the Cures Act took effect, three states – including Texas – have enacted laws permitting the delayed portal release of cancer-related and other sensitive results, giving care teams a window to reach out before a patient is left to interpret difficult news on their own.
Looking ahead
For the study’s authors, the work is a starting point rather than a conclusion. “Further study and increased interdisciplinary collaboration among oncology clinicians, health services researchers, and digital health experts can help us better understand how patients receive and react to cancer diagnoses,” Dr Bhalla said. “Our goal is to increase awareness of this issue and help drive innovative approaches to patient-centered communication.”
Source: UT Southwestern Medical Center
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AI-Supported Digital Care Improves Rheumatoid Arthritis Outcomes After Hospital Discharge
Key Takeaways:
- A nurse-led, AI-assisted digital platform reduced disease activity and improved physical function more than routine care over six months.
- People using the platform showed higher medication adherence and markedly greater satisfaction with their care.
- Real-time monitoring enabled earlier detection of problems and more personalised support between clinic visits.
The challenge of care after discharge
Rheumatoid arthritis is a long-term autoimmune condition that causes joint pain, swelling and a progressive loss of function. Managing it well once people leave hospital is often difficult, because symptoms can fluctuate and regular follow-up is not always easy to arrange. A recent real-world study set out to test whether an artificial intelligence (AI)-assisted digital care platform could improve outcomes for people living with the condition after discharge.
How the study was designed
The study, published in JMIR Medical Informatics and conducted by Ziyun Zhang, PhD, and colleagues at Tongji Hospital, followed 341 people with rheumatoid arthritis over a six-month period in a real clinical setting.
Participants were divided into two groups. One group received standard post-discharge care, while the other used a nurse-led digital management platform supported by AI. The platform allowed people to report symptoms, fatigue, medication use, laboratory results and emotional wellbeing through a smartphone app. This information was stored securely and analysed in real time. When the system detected concerning changes, healthcare staff were alerted so that they could respond quickly. Nurses and health coaches also provided ongoing education and personalised support.
The researchers focused on how disease activity, physical function, medication adherence and satisfaction changed over time, using standard clinical tools to measure disease severity and disability.
What the platform achieved
After six months, both groups showed some improvement, but the differences between them were notable. People using the AI-supported platform experienced a greater reduction in disease activity scores, meaning their arthritis was better controlled. They also showed significant improvements in physical function compared with those receiving routine care alone.
Medication adherence was higher in the digital care group, with more people taking their medicines as prescribed. Satisfaction levels were significantly higher too, with a large majority of those using the platform reporting that they were very satisfied with their care experience, compared with the standard care group.
What it means for long-term management
The authors conclude that the combination of AI monitoring, nurse-led support and continuous digital engagement helped to improve both clinical outcomes and the experience of care. The system made it easier to detect problems early, encourage medication use and provide more personalised care between clinic visits.
Overall, the study suggests that digital health platforms could play an important role in improving the long-term management of rheumatoid arthritis, particularly by keeping people more closely connected to their care teams after they leave hospital.
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Digital Health Tools Show Early Promise for Infant Feeding and Sleep, UMass Chan Research Finds
Key Takeaways:
- Families who completed three or more visits with the virtual feeding service SimpliFed provided breast milk for nearly 16 weeks longer than those who did not use it.
- Infants whose parents engaged most actively with the AI-powered sleep app Huckleberry slept around 90 minutes longer during their longest overnight stretch.
- Lower uptake among Spanish-speaking and publicly insured families underlines the need to make digital health support equitable rather than exclusionary.
Studying whether technology can support new parents
Researchers at UMass Chan Medical School are investigating whether digital tools for infant feeding, sleep and other early parenting challenges can improve health outcomes and widen access to support for families.
Among them is Nisha Fahey, DO, MSc’21, assistant professor of pediatrics and principal investigator on research examining how digital health interventions can support families during the critical first year of a child’s life. Dr Fahey has led two pilot studies evaluating virtual lactation support and an artificial intelligence–powered infant sleep application, carried out in collaboration with the Department of Medicine’s Program in Digital Medicine. That programme is led by Apurv Soni, MD, PhD’21, assistant professor of medicine and the programme’s co-director, who serves as multiprincipal investigator on the work.
As a paediatrician, Dr Fahey hears the same questions from new parents every day: Is my baby feeding enough? Are they sleeping enough? And where can I turn for help when I need it?
“These technologies already exist. Families are accessing them and using them,” said Fahey. “As researchers and healthcare providers, it’s our responsibility to understand their impact and think about how they can be integrated into healthcare in a way that is equitable and reaches all families.”
Virtual feeding support and longer breastfeeding
The first study examined SimpliFed, a virtual infant-feeding support platform that gives families on-demand access to certified lactation consultants and feeding specialists. Researchers enrolled 200 pregnant and postpartum individuals through UMass Memorial Health’s obstetrics clinics and followed them through the first year of their infant’s life.
The study assessed infant growth and development, maternal mental health, healthcare utilisation and feeding practices. Researchers found that participants who completed three or more visits with SimpliFed provided breast milk for nearly 16 weeks longer than participants who did not use the service.
The findings also drew attention to important equity considerations. Uptake was lower among Spanish-speaking families and among publicly insured participants, underscoring the need to ensure that digital health interventions reach populations that have historically faced barriers to care.
“If health systems are going to deploy these tools broadly, we need to pay special attention to making sure all patients and families can access them,” Fahey said. “The goal is to close gaps in care, not widen them.”
An AI sleep app and longer overnight rest
A second pilot study evaluated Huckleberry, a mobile app that allows parents to track infant sleep and uses artificial intelligence to predict optimal nap and bedtime schedules. This study was funded by an NIH grant focused on point-of-care technologies for heart, lung, blood and sleep disorders.
The study enrolled approximately 80 families with infants under 12 months who are beneficiaries of UMass Memorial’s MassHealth Accountable Care Organization. Participants used the app for three months while researchers tracked engagement and measured infant sleep, parental sleep and parental mental health.
Among families who engaged most actively with the app, infants experienced longer consolidated overnight sleep. Researchers found that infants in the high-engagement group slept approximately 90 minutes longer during their longest stretch of overnight sleep, compared with participants who used the app less frequently.
The researchers also found that families in a population often underrepresented in digital health research were willing and able to engage with the technology. About half of participants were classified as highly engaged users, and most reported that they found the app useful and would recommend it to other families.
Recognising the limitations
The studies also revealed some limitations. While many families reported positive experiences, others described challenges with tracking data consistently or navigating app features while caring for a young infant.
For Dr Fahey, those findings reinforce the importance of viewing digital health as a complement to, rather than a replacement of, traditional care.
“Digital technologies offer an on-demand pathway for information and support,” she said. “The goal is to make both digital and in-person care as accessible as possible and empower families to choose what works best for them.”
Building evidence for the future of care
The research was made possible through collaborations across UMass Chan, including faculty in the Program in Digital Medicine, the Department of Obstetrics & Gynecology, the Department of Psychiatry & Behavioral Health, and the Department of Pediatrics.
“Parents are seeking out digital health apps on their own,” Fahey said. “Building evidence around their benefits and understanding their limitations helps us determine whether they can become trusted parts of care in the future.”
Source: UMass Chan Medical School
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Digital Health Tools Are Now a Routine Part of Everyday Care in the US
Key Takeaways:
- A landmark review of more than 8 billion interactions across US healthcare finds that online portal messaging has become a standard, everyday part of care rather than an occasional add-on.
- Digital communication is supplementing in-person medicine, not replacing it – office visits have rebounded to two to three per patient each year while portal messages have more than doubled.
- Researchers warn that the growing digital workload sits on top of clinicians’ existing duties, raising new questions about staffing, training and the role of AI support tools.
A new picture of how Americans reach their clinicians
At least 12 per cent of people in the United States now contact their healthcare providers about appointments, test results and ongoing treatments through secure online patient portals and health apps, according to a major new study. At the same time, traditional in-person visits to the doctor’s office have recovered from their pandemic-era decline. The findings suggest that while digital medicine has become a routine feature of care, it is adding to in-person services rather than displacing them – an evolution that researchers say is reshaping how hospitals and clinics run day to day.
These are the central conclusions of a study led by researchers at NYU Langone Health, described as the largest review ever conducted of communications recorded in Epic electronic health records. The team analysed more than 140 million patient records drawn from 2,067 hospitals and 47,100 health clinics across the US, examining over 8 billion interactions between patients and providers that took place between January 2020 and December 2025.
What the data showed
Published online in the Journal of the American Medical Association (JAMA) on 22 June, the study found that online portal messages more than doubled between 2020 and 2025, rising by 153 per cent. Over the same period, total telephone calls fell by 6 per cent. The number of people with an active Epic health record climbed from 94 million in 2020 to 140 million in 2025. During the first three months of 2025, 30 per cent of active Epic patients – some 42 million people – sent a portal or health app message to their clinician.
Crucially, this surge in portal activity is not coming at the expense of face-to-face care. In-office visits have returned to an average of between two and three per patient each year. Messages from patients to their providers have, meanwhile, doubled since the pandemic, increasing from an average of 2.2 per year in early 2020 to 5.4 per year in late 2025.
“Our study shows that use of patient portals, health apps, and messaging are now a routine part of everyday patient care across America, not simply side channels used occasionally,” said study senior investigator Michal A. Mankowski, PhD.
Dr Mankowski, an assistant professor in the Department of Surgery at NYU Grossman School of Medicine, said the findings show that people now have far more direct access to physicians and other clinicians than before.
“Our findings reveal that while digital health tools have become a core part of healthcare, delivery is becoming more continuous and timeless, and no longer tied to scheduled appointments during routine work hours,” said Dr Mankowski.
The scale of digital care since 2020
The review also quantified the sheer volume of activity logged through Epic record systems since 2020. Over that period, people in the US booked at least 1.77 billion in-person visits to health clinics, sent 1.34 billion messages to their providers and received roughly 3.25 billion portal messages from providers in return. Epic systems also documented 1.59 billion telephone calls and 146 million virtual telehealth portal visits.
A new layer on top of clinical work
Study co-investigator Dorry L. Segev, MD, PhD, said the digital delivery of healthcare does not replace established ways of working; rather, it adds a further layer of steps to existing workflows. To cope with this new reality, he argued, hospitals, clinics and healthcare workers will need to plan ahead for staffing and support.
“Modern delivery of healthcare means increasingly that healthcare providers will have to balance their digital workload on top of their traditional clinical workload,” said Dr Segev, a professor and vice chair in the Department of Surgery at NYU Grossman School of Medicine.
“Clinical staff will need to be trained in mastering the tools of messaging in healthcare; in using AI support programs, including chatbots that can frame content to minimize its complexity; and in making the most effective use of clinician time needed for online billing and online counseling,” added Dr Segev, who is also a professor in NYU Grossman’s Department of Population Health.
He noted that NYU Langone already uses AI support tools to speed up the drafting of physician and provider notes. Looking ahead, Dr Segev said the team plans to examine digital-use trends within individual healthcare systems, including NYU Langone, in order to identify regional and outpatient clinic-specific shifts that could affect operational planning.
How the study was carried out
For the research, the team drew on Epic Cosmos, a national dataset containing the electronic health records of more than 300 million patients in the US. The dataset includes information from a majority of the hospitals and clinics that use Epic, the country’s largest vendor of electronic health record systems. Epic had no role in carrying out the study. Funding was provided by NYU Langone.
Alongside Dr Mankowski and Dr Segev, the NYU Langone researchers involved were lead investigator Jane J. Long, MD, and co-investigators Mara A. McAdams DeMarco, PhD; Mark D. Schwartz, MD; Joshua Chodosh, MD; and Eric K. Oermann, MD.
Disclosures
Dr Mankowski was recently elected to serve on the governing board of Epic Cosmos. Dr Schwartz reported being president-elect of the Society of General Internal Medicine. Dr Segev has received consulting and/or speaking honoraria from Sanofi, CareDx, Moderna, AstraZeneca, Roche, Optum, OrganOx, Hansa and Biosidus, and is a journal editor for Springer. None of these activities are related to the current JAMA study. NYU Langone is managing the terms and conditions of these relationships in accordance with its policies and procedures.
Source: NYU Langone
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Autonomous AI Agent Matches and Exceeds Physicians Across Simulated Electronic Health Record Cases
Key Takeaways:
- MIRA is an autonomous AI agent that diagnoses and plans treatment inside a simulated electronic health record, rather than acting as a narrow chat tool.
- It reached 88.9% diagnostic accuracy across 574 cases, outperforming board-certified physicians (78.1%) and a mixed-seniority team (71.1%).
- Safety results were strong but preliminary, and the authors stress that MIRA is not a replacement for human clinicians.
A new kind of medical AI agent
A recent study published in the journal Nature introduced MIRA, an autonomous AI agent designed to operate within sandboxed EHR environments. Rather than acting as a single-purpose assistant, MIRA uses a suite of digital tools to simulate the full arc of a clinical workflow. It can order tests, synthesise the results, and produce diagnoses and treatment plans, all while communicating through a chat interface with a patient AI agent that is grounded in the documented history of present illness extracted from retrospective notes from genuine cases.
The system runs on a Fast Healthcare Interoperability Resources (FHIR) based architecture, which executes the agent’s tool calls and records its medical outputs. The researchers note that the example data presented in the paper were shortened and slightly modified to comply with the privacy restrictions attached to the dataset.
Unlike earlier implementations, which were predominantly task-specific chat applications, MIRA was built to independently take in patient histories, order the relevant diagnostic tests, and then use those datasets to reach diagnoses and treatment plans within a controlled simulation. Across the 574 MIMIC-IV cases, MIRA achieved 88.9% diagnostic accuracy, and in a matched 311-case physician comparison it reached 87.8% accuracy, significantly outperforming experienced human physicians under identical simulated conditions while demonstrating strong, though not perfect, safety and guideline performance.
Background: from passing exams to working a ward
Large language models (LLMs) have already proven highly capable at passing standardised medical examinations and answering complex clinical questions. Reviews of the field show, however, that translating this raw clinical knowledge into the operational workflow of a hospital has remained a major challenge.
This gap is attributed to the architectural design of traditional medical AI tools, which behave as narrow, task-specific search or text-generation utilities rather than as active partners in care. By contrast, true clinical decision-making is characterised as an intricate, multi-step process in which doctors repeatedly interview the people in their care, order blood tests or imaging, synthesise conflicting results, and update their hypotheses before arriving at a final treatment plan.
Nearly all of this clinical work takes place within EHR systems that rely on complex, standardised coding protocols. Until now, it remained unproven whether an automated system could reliably handle this end-to-end clinical action space in a realistic, EHR-style environment without committing unacceptable errors.
About the study
The study set out to address this functional gap by developing MIRA, a novel AI tool designed to autonomously ingest and access medical records, identify knowledge gaps, and order diagnostic tests to supplement the EHR record, before using the completed dataset to recommend clinical interventions.
The researchers then tested MIRA’s capabilities in a sandboxed, virtual EHR environment compliant with standard healthcare protocols, including HL7 FHIR. The sandboxed test was conducted on a curated benchmarking dataset of 574 real-world emergency department cases from the Medical Information Mart for Intensive Care (MIMIC-IV) database.
The cases included spanned eight distinct diagnoses across surgery (appendicitis), internal medicine (pneumonia), and oncology (pancreatic cancer), which MIRA navigated using 11 specialised digital tools offering more than 85,000 operational choices. The agent was permitted to request physical examinations, order targeted laboratory values, look up medical histories, and generate medication orders within the simulated EHR, rather than in live patient care.
How MIRA was compared with clinicians
MIRA’s output was compared against two distinct groups of human physicians managing exactly the same cases under identical conditions. The first group was a cohort of four board-certified physicians. The second was a mixed-seniority team consisting of four residents and two board-certified doctors.
A separate, conventional text-based AI agent was used to simulate the people under MIRA’s care, and under the care of the human physician teams. This agent was instructed to respond to questions posed by MIRA or its human counterparts solely on the basis of authentic clinical histories, while resisting adversarial attempts to trick it into prematurely leaking information. The authors noted, however, that simulated patient speech may be more structured than real emergency department conversations.
Study findings
The results revealed that MIRA performed at or above the level of experienced human doctors. It achieved 88.9% diagnostic accuracy across the full 574-case dataset and 87.8% accuracy in the matched 311-case physician comparison. By comparison, the board-certified physicians reached an average accuracy of 78.1% (p < 0.001), while the mixed-seniority medical cohort averaged 71.1% (p < 0.001).
MIRA was found to excel at identifying appendicitis and pancreatitis, achieving a perfect 100% recall for laparoscopic appendectomies. For pancreatic cancer, its diagnostic performance was equivalent to that of the board-certified physicians, while pneumonia and urinary tract infections remained more challenging.
Accuracy without simply “ordering everything”
Notably, MIRA did not achieve its superior accuracy by simply “ordering everything”. While it was observed to request a broader, more comprehensive set of individual blood parameters than the human doctors, its overall test selection remained well below the historical baselines recorded in the dataset.
The findings further demonstrated that the model successfully avoided the systematic over-ordering of high-cost radiological imaging, matching or exceeding physicians on overall resource-alignment metrics.
Safety performance
The safety evaluations were similarly encouraging, though still preliminary. An independent, blinded medical review of 56 patient-level outputs, together with a separate assessment of 468 prescriptions written by MIRA, established that the agent caused zero high-severity drug–drug interactions, zero renal dosing incompatibilities, and zero medication-allergy mismatches. Route specification was the weakest prescription field, at 97% correctness.
When making critical hospital admission decisions for pneumonia and pulmonary embolism, MIRA achieved a perfect recall score of 1.00, indicating that it never missed a single person who required inpatient care. The pulmonary embolism analysis did, however, suggest a tendency towards over-admission, reflecting a cautious disposition strategy.
Conclusions
The study introduces an integrated EHR AI agent, MIRA, that successfully translates clinical intents into structured, safe, and accurate operations, with the potential to support physicians in their work. The authors are careful to caution, however, that MIRA and similar AI agents are not replacements for expert human staff.
The model did not reach 100% perfection across all treatment choices, such as specific antibiotic selections, which highlights the ongoing need for strict human supervision and patient-level safeguards. Future iterations of the model may improve their performance by incorporating evidence from retrieval-based support, stronger governance, and prospective real-world validation before any clinical deployment.
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AI Blood Test Could Detect Early Eye Nerve Damage in People with Type 2 Diabetes
Key Takeaways:
- An AI tool called Pro-DRN uses a blood sample to flag people with type 2 diabetes at high risk of diabetic retinal neurodegeneration (DRN), before any damage shows on the retina.
- It was trained on 1,218 participants and validated in 502 people from UK Biobank, identifying 71 proteins linked to DRN – with ACTA2, COL6A3 and HSPG2 the strongest predictors.
- As retinal nerves are among the first tissues affected by diabetes, the test could also hint at wider nerve damage and help target earlier monitoring and future treatments.
A simple blood test to catch nerve damage early
Scientists have developed an AI-assisted prediction tool that can identify people with type 2 diabetes who are at high risk of developing diabetic retinal neurodegeneration (DRN) before symptoms appear. The findings were published in the journal PLOS Medicine.
The work was led by Wei Wang, MD, PhD, associate professor at the Guangdong Provincial Clinical Research Center for Ocular Diseases. According to the authors, the damage that diabetes inflicts on the delicate nerves of the eye appears to leave a detectable molecular trail in the bloodstream long before it becomes visible in the eye itself.
“Our study suggests that early retinal nerve damage in diabetes leaves measurable signals in the blood,” write the authors. “These findings suggest that a simple blood test analyzed with artificial intelligence may help identify people with diabetes who are at highest risk of early retinal nerve damage, well before visible damage appears on the retina.”
Why the retinal nerves matter in diabetes
Type 2 diabetes affects more than half a billion people worldwide, and it carries an increased risk of long-term complications, including progressive neurodegeneration – the gradual deterioration of nerve tissue over time.
The nerves of the retina are among the earliest tissues to be affected. As this damage advances, it can eventually lead to severe visual impairment and the loss of sight. The difficulty for clinicians is one of timing: current diagnostic methods can only detect DRN once the retina has already sustained irreversible damage. By the time the problem is visible, the window for early, protective intervention has often closed.
How the Pro-DRN tool was built
To address this, Wang and colleagues developed a machine learning algorithm called Pro-DRN. They drew on data from 1,218 participants in the Guangzhou Diabetic Eye Study, all of whom had been diagnosed with type 2 diabetes but had not yet developed DRN at the point of enrolment.
The model combined two distinct streams of information. The first was proteomics data – a detailed read-out of the proteins circulating in participants’ blood samples. The second was a series of yearly retinal images, capturing the state of the eye over a six-year follow-up period. By matching the molecular signals in the blood against how each person’s retina changed year on year, the algorithm learned which blood-borne patterns preceded the onset of nerve damage.
The proteins behind the predictions
The analysis surfaced 71 proteins associated with the development of DRN. Of these, three stood out as the most consistent drivers of accurate prediction: ACTA2, COL6A3 and HSPG2. These are key structural components involved in maintaining the integrity of the nerve and muscle tissue in the eye, which helps explain why disturbances in their levels might signal nerve tissue under strain.
Crucially, the team did not rely on a single dataset. The results were validated in an independent cohort of 502 people from UK Biobank, where the core effects and protein signals were reproduced – an important check that the findings were not simply a quirk of the original group.
From research tool to clinical aid
Pro-DRN has been made available as an interactive, web-based risk assessment tool that clinicians can use to support early DRN screening and to monitor how a person’s risk evolves over time. People identified as being at high risk could then benefit from more frequent check-ups and from early interventions aimed at preventing or slowing progressive neurodegeneration, rather than waiting for damage to become apparent.
A window into the wider nervous system
The potential significance of the test reaches beyond the eye. Because DRN is one of the first signs of nerve degeneration brought on by diabetes, detecting it early could also signal the onset of nerve injury elsewhere in the body.
Such damage can contribute to cognitive impairment, dementia and peripheral neuropathy – the latter causing loss of sensation and motor control in the hands, feet and other extremities. Viewed this way, a single eye-focused test could offer valuable insight into the overall health of a person’s nervous system.
New possibilities for treatment and trials
The discoveries also open up two further avenues. The proteins identified as being involved in DRN progression could be investigated as potential targets for the development of new therapies. In addition, the AI-based tool could prove useful for selecting and stratifying participants in clinical trials that are evaluating neuroprotective strategies designed to prevent or delay nerve damage – helping ensure such studies enrol the people most likely to show a measurable benefit.
Looking ahead
For the researchers, the broader ambition is a shift in how diabetic eye care is approached – from reacting to damage that has already occurred towards anticipating who is most vulnerable.
“Pro-DRN may help move diabetic eye care from detecting established damage toward earlier, molecularly informed risk stratification, so that closer monitoring and future neuroprotective interventions can be directed to the people most likely to benefit,” Wang and colleagues write.
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Half a Million NHS Staff to Receive AI Tools to Free Up More Time for Patients
Key Takeaways:
- NHS England is rolling out Microsoft 365 Copilot to 505,000 clinicians and support staff, with the full rollout expected to be complete by October 2026.
- The world’s largest healthcare AI trial of its kind found that the tool could save staff an average of 43 minutes a day – the equivalent of around five weeks a year, or roughly two days of administrative time every month.
- Copilot is expected to support a wide range of roles, including clinical administration, ward clerks, medical secretaries, core services such as HR and finance, and management.
A major step forward in NHS AI adoption
More than half a million NHS staff are being given access to new artificial intelligence (AI) tools that could free up an average of two days every month from administrative duties, creating more time for the work that matters most to patients and staff.
NHS England announced today that it is significantly accelerating the adoption of AI across healthcare services by providing 505,000 clinicians and support staff with access to Microsoft 365 Copilot.
The AI personal assistant is designed to help clinicians draft documents and analyse data more efficiently, so they can devote more of their time to caring for patients.
What the world’s largest healthcare AI trial found
The agreement follows the largest AI trial of its kind anywhere in the world in healthcare, which gave more than 30,000 NHS workers across 90 NHS organisations access to Microsoft 365 Copilot.
The trial found that AI-powered administrative support could save an average of 43 minutes per staff member each day, or more – the equivalent of five weeks of time per person every year.
Results from the trial indicated that a full rollout of Microsoft 365 Copilot could save millions of hours of staff time per month.
NHS England and government leaders welcome the rollout
Rob Thompson, Chief Digital, Data and Technology Officer at NHS England, said: “The NHS wants to embrace cutting-edge technology and this Microsoft partnership will mean staff can be freed from admin so they can focus more of their time on what matters most – improving care for patients.
“Innovations like this will help drive NHS productivity so patients can get the treatment they need sooner and there is better value for taxpayers.
“The potential to save NHS staff around 2 days of admin time every month could be a gamechanger for patients.
“As part of our 10 Year Health Plan, we’re making sure every pound is spent on cutting waiting times and boosting care”.
Health Innovation and Safety Minister Preet Kaur Gill said: “Technology should support our NHS staff, not slow them down.
“Every day, doctors, nurses and other healthcare professionals spend valuable time on administrative tasks that take them away from patients. By rolling out Microsoft Copilot across the NHS, we can reduce that burden, free up clinicians’ time and help staff focus on what they do best caring for patients.
“This government is putting innovation to work for patients: helping staff work more efficiently, improving productivity and supporting a modern NHS that delivers better care, faster access to treatment and better value for taxpayers”.
Darren Hardman, CEO, Microsoft UK and Ireland, said: “By rolling out Microsoft 365 Copilot at scale, NHS teams can cut through everyday admin and spend more time where it matters most.
“Bringing AI safely into the flow of healthcare will help ease pressures, improve productivity and support better decision-making across the health service.
“We’re proud to work with NHS England to help tackle some of its biggest challenges and accelerate digital transformation for the benefit of staff and patients alike”.
How Copilot will be used across the health service
Copilot is designed to help users create, analyse and complete work more quickly. NHS England anticipates that it will be harnessed in a variety of ways across all aspects of the healthcare service, including:
- Clinical administration: supporting clinicians in drafting letters and in registrar training.
- Ward clerks: assisting with patient discharge processes, service data analysis, rota building and bed management.
- Medical secretaries: helping to draft patient letters and meeting minutes, and creating templates to maintain consistency.
- Core services: supporting human resources, finance and procurement functions.
- Management: assisting with drafting board papers, briefings and organisational analysis.
Licensing and timeline for the rollout
Each NHS trust will receive a central allocation of licences based on its organisational headcount, typically starting at around 2,000 Microsoft 365 Copilot licences.
The rollout to more than 500,000 staff across the NHS is expected to be complete by October 2026.
Source: NHS England
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People with Obesity Tend to Move Less After Starting GLP-1 Medications, Study Finds
Key Takeaways:
- Daily step counts and moderate-to-vigorous activity both dropped after adults with obesity started a GLP-1 receptor agonist, with no sign that weight loss prompted people to move more.
- Because these medications strip away lean muscle as well as fat, staying active matters for protecting strength and long-term health rather than being an optional extra.
- This is the first large study to draw on data from wearable fitness trackers in adults taking GLP-1 medications, and its authors argue for targeted support that builds activity in alongside treatment.
A counterintuitive picture of how people move
It is tempting to assume that as the weight comes off, people naturally become more active. New findings suggest the opposite may be closer to the truth. Adults with obesity who were losing weight on glucagon-like peptide-1 (GLP-1) receptor agonist medications significantly reduced their physical activity, according to a study being presented on Saturday at ENDO 2026, the Endocrine Society’s annual meeting in Chicago, Illinois.
That matters because activity is one of the main safeguards against an unwanted side effect of these treatments. GLP-1 receptor agonists such as semaglutide, liraglutide, dulaglutide and tirzepatide reduce not only fat but also lean muscle mass. This makes physical activity essential for preserving strength and long-term health, according to study lead Sajana Maharjan, M.D., of HSHS St. John’s Hospital in Springfield, Illinois.
How the study was carried out
The work was a retrospective pre–post cohort study, meaning researchers compared the same individuals before and after they started treatment. It drew on data from the National Institutes of Health’s All of Us Research Program, which links participants’ electronic health records with their Fitbit activity data, allowing the team to track real-world movement rather than relying on self-reported habits.
Among the 1,950 adults with obesity who started a GLP-1 medication, researchers studied 753 people who had enough wearable-device data for analysis. The cohort was predominantly female, at 78.6 per cent, with a mean age of 52.7 years.
For each person, the researchers compared physical activity before and after treatment began, focusing on two measures: daily step counts and minutes of moderate-to-vigorous physical activity (MVPA).
Steps and active minutes both fell
The direction of travel was clear. On average, daily steps decreased from 5,047 to 4,487 per day, while MVPA minutes fell from 28 to 22 per day after people began a GLP-1 receptor agonist medication.
Crucially, the study found no evidence that weight loss from these medications led to increased physical activity. The expected rebound in movement simply did not appear in the data.
Who saw the biggest changes
The decline was not evenly spread. The largest drops were seen in men and in people living with joint or muscle pain. By contrast, factors such as age, heart failure or a prior stroke did not change the results, suggesting the pattern held across a fairly broad range of circumstances.
Why activity cannot be an afterthought
For Dr Maharjan, the practical message is that exercise needs to be designed into treatment rather than left to chance:
“While many assume that weight loss leads naturally to increased physical activity, our study suggests otherwise. The findings in our study reinforce that exercise cannot be optional for people taking these medications. People need targeted interventions that encourage physical activity alongside medication for obesity.”
Given that GLP-1 receptor agonists reduce lean muscle alongside fat, a fall in activity could compound the loss of strength, making structured support for movement an important part of care rather than a nice-to-have.
A first for wearable-data research
The study stands out for its method as much as its findings. It is the first large study analysing data from wearable fitness trackers among adults taking GLP-1 receptor agonists, offering a more objective window into everyday behaviour than questionnaires alone can provide. As these medications become more widely used, that kind of real-world evidence is likely to shape how clinicians and patients approach physical activity during treatment.
CCH insights:
This is a very interesting study, but it throws up more questions than answers. Firstly, were any of the participants receiving diet and lifestyle advice as they are supposed to? GLP-1 medications are designed as an adjunct to such advice, but these results suggest it was probably lacking from these patients’ treatment. Another question, of course, is why did physical activity drop? Further research is needed to understand what is the underlying reason for these results. But most importantly, this study is a reminder that GLP-1 therapy is not just about taking the medication, it requires diet and lifestyle advice and ongoing support and monitoring.
Source: Endocrine Society
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