Digital Tools Show Promise in Supporting Children and Teenagers to Build Healthier Habits
Key Takeaways:
- A large global analysis involving more than 133,000 children and teenagers shows that digital health tools can support improvements in physical activity, diet, sedentary behaviour and weight outcomes.
- Mobile applications appear to have the strongest influence on diet and weight, while wearable devices are especially effective in reducing sedentary time.
- Shorter programmes are most effective for increasing activity, whereas longer programmes deliver stronger effects on weight management.
Introduction
Concerns about excessive screen time and mobile phone use are common among parents, and technology is often cited as a cause of declining health among children and teenagers. However, new research from the University of South Australia suggests that digital technology may also play a constructive role in helping young people adopt healthier behaviours.
This research represents the largest global analysis to date examining how digital tools affect health outcomes among people under the age of 18. Drawing on data from more than 133,000 children and teenagers worldwide, the study indicates that mobile health applications, wearable devices and interactive digital programmes can support improvements in physical activity, dietary intake and weight-related outcomes.
How digital tools influence health behaviours
Increased physical activity
The review found that children and teenagers who used digital health tools engaged in more overall physical activity. The observed increases were most notable in moderate and vigorous activity, equivalent to approximately 10 to 20 additional minutes of moderate-to-vigorous physical activity per day.
Improved dietary choices
Digital programmes and applications also helped young people increase their intake of fruit and vegetables and reduce the consumption of high-fat foods.
Positive effects on weight
Although the changes were modest, the analysis showed consistent improvements in body weight and body fat levels among participants who used digital health tools.
Reduced sedentary time
Some interventions, particularly those involving wearable technology, helped participants spend 20 to 25 fewer minutes per day sitting or engaging in screen-based sedentary activities.
Limited impact on sleep
The study found no clear evidence that digital health tools improved sleep duration or quality.
Which tools work best?
The analysis distinguished between different types of digital interventions:
- Mobile applications had the strongest effects on dietary improvements and weight-related outcomes.
- Wearable devices, such as fitness trackers, were most effective in reducing sedentary time.
- Programme length also played a role: shorter programmes of eight weeks or fewer were most effective for increasing activity levels, while longer programmes of twelve weeks or more had a greater impact on weight management.
Expert perspective
Lead researcher Dr Ben Singh from the University of South Australia emphasised the potential of electronic health (e-Health) and mobile health (m-Health) platforms to support healthier lifestyles among young people.
“Even though most young people know the importance of eating well, exercising regularly, and getting enough sleep, many still fall short of the recommended health guidelines, putting them at greater risk of obesity, diabetes, and heart disease,” Dr Singh said.
“Digital health tools such as wearables, fitness apps, and online programmes could help turn this around by motivating kids to be more active and eat better.
“Our research shows that digital health tools and apps can significantly improve children’s physical activity, diet and weight outcomes, putting them on a better health trajectory for life.
“Because children and teens have grown up with technology, they’re naturally open to using apps. They’re accessible, engaging, and easy to scale, which makes them a great choice for schools and community programmes to promote healthier lifestyles.”
The global context
According to the World Health Organization, 80 per cent of teenagers do not meet recommended levels of physical activity. Globally, 390 million children aged 5 to 19 years are classified as having overweight, including 160 million with obesity. In Australia, one in five children fall into the categories of overweight or obesity, and fewer than a quarter of children aged 5 to 14 achieve the recommended hour of daily physical activity.
About the research
This investigation was a systematic umbrella review and meta-meta-analysis. It synthesised findings from 25 systematic reviews to assess the impact of a wide range of digital tools, including mobile applications, wearable devices, text messaging programmes, active video games and web-based platforms. The outcomes assessed included physical activity, sedentary behaviour, sleep, dietary intake and weight.
Implications for policy and education
Dr Singh stated that policymakers and educators could use these findings to guide the integration of digital tools into strategies that support young people’s wellbeing.
“We know that features such as gamification, tailored messaging, and machine learning can boost engagement,” Dr Singh said.
“By integrating evidence-based apps and wearables into schools, primary care and community programmes, we can make healthy habits more appealing and accessible for young people.
“This review brings together global evidence to understand when and how these tools work best. Short bursts of programmes are ideal for lifting activity levels, while longer ones are better for weight management.
“These online tools worked as well as, and sometimes better than, traditional in-person health programmes.
“Combining digital tools with light human support – from teachers, parents or health coaches – can also help keep motivation high.
“If we can encourage the use of healthy digital tools from a young age, we have a real opportunity to help children and teens form healthier habits that last a lifetime.”
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UMass Chan Launches National Collaborating Centre to Study Digital Lifestyle Interventions for People Using GLP-1 Medications
Key Takeaways:
- A major trial at UMass Chan will test whether a digital lifestyle change programme enhances outcomes for people using GLP-1 therapies to manage obesity, diabetes or cardiovascular disease.
- The work launches a new CDC-funded centre dedicated to lifestyle change implementation research, with a four-year award of 2 million dollars.
- Researchers aim to strengthen scientific evidence on how structured lifestyle interventions can support people taking GLP-1 medicines in real-world settings, including effects on physical activity, diet, muscle mass, adherence and quality of life.
Launch of a new national collaborating centre
UMass Chan Medical School has initiated a large research programme to examine whether a digital lifestyle change intervention can improve outcomes for people using GLP-1 therapies to manage obesity, diabetes or cardiovascular disease. This project marks the launch of the Lifestyle Change Implementation Research Network Collaborating Center at UMass Chan’s Prevention Research Center. The centre is supported by a four-year, 2 million-dollar award from the United States Centers for Disease Control and Prevention.
The project is jointly led by Jamie Faro, PhD, assistant professor of population and quantitative health sciences, and Stephenie C. Lemon, PhD, the Barbara Helen Smith Chair in Preventive and Behavioural Medicine, professor of population and quantitative health sciences, chief of the Division of Preventive and Behavioural Medicine, and co-director of the Prevention Research Center at UMass Chan.
Understanding the early experience of people using GLP-1 therapies
Dr Faro said: “We are going to look at what patients using GLP-1s are experiencing from early on in their journey, including changes in physical activity, diet, skeletal muscle mass, side-effect management, medication adherence and quality of life. We are hopeful this study addresses how lifestyle change interventions can impact these areas when implemented alongside patient’s medication.”
The research team intends to evaluate how a structured, digitally delivered programme may support people who are navigating the rapid physiological and behavioural changes often associated with GLP-1 therapy.
Study design and participant experience
Recruitment is expected to begin in early 2026, focusing on people living in the Worcester area. The study will enrol 220 participants and compare outcomes for individuals using the Noom Weight digital lifestyle change programme and Noom’s GLP-1 Companion with those receiving standard care. People who are allocated to standard care will have the option of accessing the digital intervention once the study concludes.
Participants in both groups will receive a wearable device to monitor physical activity over an eight-month period. They will also complete a series of lifestyle and health questionnaires, including dietary recalls. The dietary assessments will be led by co-investigator Sabrina Noel, PhD, RD, associate professor of biomedical and nutritional sciences and director of the Center for Population Health and the Health Assessment Laboratory at UMass Lowell.
Building the evidence base for real-world practice
Dr Faro emphasised the lack of robust data on how structured lifestyle change programmes can support people in real-world settings. She said: “There needs to be more scientific evidence on how lifestyle change interventions can support patients’ needs in real-world settings. The team laid the groundwork for this project by conducting pilot projects in UMass Memorial Health clinics, funded by the UMass Chan Ambulatory Research Consortium and the Mel Cutler pilot award in the Department of Population and Quantitative Health Sciences.”
The project will also investigate how lifestyle interventions can be implemented across different levels of the health system, including within clinical settings and by providers and payors.
Addressing risks and supporting long-term needs
Dr Lemon highlighted the importance of ensuring people using GLP-1 therapies receive appropriate lifestyle support. She said: “We want to establish evidence that can be applicable in other contexts that helps patients understand and engage in these necessary lifestyle interventions. Otherwise, we are going to have a population of GLP-1 users who lose weight but lose their muscle mass or have other issues that could be helped with lifestyle interventions, or who come off these meds and need additional support as they regain weight.”
A national network with shared goals
UMass Chan is one of four funded sites to receive a Lifestyle Change Interventions Research Network Coordinating Center Special Interest Project award from the CDC. The other sites include the University of Utah, the University of Pittsburgh and the University of South Carolina. Each site is conducting its own research project tailored to the evidence gaps identified by the CDC and to the needs of its local population.
The new centre at UMass Chan will collaborate closely with the CDC’s Coordinating Center and with Prevention Research Centers across the national network. Together they aim to advance research and practical implementation, with a focus on sustainable, evidence-based lifestyle change interventions to reduce obesity, diabetes, cardiovascular disease and related chronic conditions.
Dr Lemon summarised the broader ambition of the network: “The goal of the network is to bring together researchers and practitioners from across the country who are interested in this field, with a goal of building knowledge and capacity for implementing advanced weight loss interventions and potentially doing small scale additional research studies that fill evidence gaps in partnership between researchers and practitioners.”
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AI Tool Creates ‘Digital Twins’ of Patients to Forecast Future Health
Key Takeaways:
- New DT-GPT model creates virtual patient replicas to predict individual health trajectories with notable accuracy.
- The model outperformed 14 leading machine learning systems and demonstrated effective zero-shot predictions.
- Technology could accelerate drug development and shift healthcare towards more predictive and personalised practice.
Introduction
A new artificial intelligence model capable of generating virtual patient representations and forecasting future health outcomes has been described as a potential breakthrough for clinical research. The system, developed by researchers at the University of Melbourne, uses large language model (LLM) techniques to create personalised digital twins that mirror each individual’s clinical profile.
How the DT-GPT model was developed
The research team trained an existing large language model on three extensive datasets containing thousands of electronic health records. These datasets included information on people living with Alzheimer’s disease, people with non-small cell lung cancer, and people admitted to intensive care units. The aim was to equip the model with sufficient breadth of clinical data to enable it to generate detailed patient-level predictions.
The resulting tool, named DT-GPT, analysed each person’s medical history, such as laboratory values, diagnoses, and treatments. Using this information, it constructed a virtual counterpart for every individual and projected how their condition might evolve under ongoing clinical care.
Predictive performance and validation
Crucially, the model was not shown any actual health outcomes during training. This allowed researchers to rigorously assess the accuracy of its predictions once the model generated forecasts.
Associate Professor Michael Menden, lead researcher, explained the approach:
“For each patient, we created a virtual replica by initialising the model with their individual clinical profile.”
He added:
“For example, we created virtual twins of 35,131 intensive care unit (ICU) patients and accurately predicted what would happen to their magnesium levels, oxygen saturation and their respiratory rate over a 24 hour period, based on their laboratory results from the previous day.”
When benchmarked against 14 state-of-the-art machine learning models, DT-GPT consistently outperformed them in predictive accuracy.
Implications for clinical trials and personalised medicine
Researchers believe the tool has significant implications for the future of clinical trials. Because the model can simulate potential outcomes for large groups of virtual participants, it may help streamline drug development processes by reducing time and cost associated with early-stage testing.
Associate Professor Menden said:
“This technology paves the way for a shift from reactive to predictive and personalised medicine.”
He continued:
“It could enable doctors to anticipate if their patient’s health will deteriorate so they can intervene earlier.
“It could also be used to predict negative side effects of medications, allowing doctors to tailor treatment plans to suit each patient’s unique characteristics and medical history, ultimately increasing the chances of a positive health outcome.”
Conversational interface and handling of complex data
One of DT-GPT’s strengths is its ability to interpret large volumes of complex, unstructured clinical data. The system also includes a conversational interface that functions similarly to a chatbot, enabling clinicians and researchers to query the model directly and explore the reasoning behind specific predictions.
Zero-shot predictions: an advanced capability
Because DT-GPT is based on generative AI, it can also perform zero-shot predictions. These are informed estimates of clinical values that the model has not been explicitly trained to predict.
Associate Professor Menden illustrated this:
“To use an analogy, it’s like asking the model to predict how tall someone will grow without providing the person’s height records and only giving their previous weight and shoe sizes.”
He noted a key finding:
“Our model accurately predicted how lactate dehydrogenase (LDH) levels changed in non-small cell lung cancer patients 13 weeks after they started therapy, despite not training the model for this purpose.
“We compared it to traditional machine learning models, which were specifically trained for 69 clinical variables, including LDH, which we in comparison only educated guessed.
“Very surprisingly, the DT-GPT’s zero-shot predictions, its untrained guesses, were more accurate in 18 percent of cases.”
The study was recently published in NPJ Digital Medicine.
Next steps: expanding to other conditions
The team responsible for developing DT-GPT, in collaboration with the Royal Melbourne Women’s Hospital, have now established the foundation for a new company that will apply digital twin technology to support people living with endometriosis. This work highlights the potential wider applicability of the model across different medical conditions.
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New AI Model Predicts Donor Viability and Could Cut Wasted Organ Transplant Efforts by 60%
Key Takeaways:
- A new machine learning model developed at Stanford University predicts whether a donor is likely to die within the critical timeframe needed for safe organ recovery.
- The system reduced futile liver procurement attempts by 60% and outperformed senior transplant surgeons.
- The tool could improve efficiency, reduce resource waste and expand access for people waiting for a donor organ.
A data-driven approach to a long-standing challenge
Thousands of people worldwide remain on transplant waiting lists, with demand far exceeding the supply of suitable donor organs. For people who require a liver transplant, recent advances have broadened access by enabling the use of donors who die following cardiac arrest. These cases, known as donations after circulatory death (DCD), have significantly increased potential donor numbers.
However, almost half of DCD liver transplant procedures are cancelled. In every case, timing is critical. After life support is withdrawn, the donor must die within 45 minutes to protect liver viability. If death occurs outside this narrow window, surgeons often reject the organ because of the increased risk of complications for the recipient.
This contributes to substantial resource waste, operational strain on transplant centres and missed opportunities for people waiting for life-saving surgery.
A new predictive tool outperforms top surgeons
Researchers, clinicians and scientists at Stanford University have developed a machine learning model designed to improve prediction accuracy around donor viability. The tool estimates whether a donor is likely to die within the period during which their organs remain suitable for transplantation.
The model surpassed the predictions of highly experienced surgeons and reduced the rate of futile procurements by 60%. Futile procurements occur when surgical teams begin preparing for a transplant but cannot proceed because the donor dies too late for the organ to remain viable.
Dr Kazunari Sasaki, clinical professor of abdominal transplantation and senior author of the study, explained the significance of the advance. “By identifying when an organ is likely to be useful before any preparations for surgery have started, this model could make the transplant process more efficient,” he said. “It also has the potential to allow more candidates who need an organ transplant to receive one.”
The findings were published in The Lancet Digital Health.
How the model works
The machine learning tool was trained using data from more than 2,000 donors across multiple US transplant centres. It analyses neurological, respiratory and circulatory indicators to estimate a donor’s progression towards death more accurately than previous tools or clinical judgment alone.
During retrospective and prospective testing, the model maintained strong predictive accuracy even when some donor data were missing. Researchers emphasised that this makes it especially practical for real-world clinical settings, where data completeness can vary.
Addressing resource strain and improving outcomes
Currently, transplant centres primarily rely on surgeons’ judgment to assess whether a donor is likely to die within the necessary timeframe. These predictions can vary considerably and may lead to unnecessary preparation of operating theatres, mobilising teams and allocating resources that ultimately go unused.
A reliable, data-driven tool has the potential to improve decision-making, reduce operational burden and ensure that efforts are more closely aligned with the likelihood of a successful transplant.
As the research team noted, the model demonstrates “the potential for advanced AI techniques to optimise organ utilisation from DCD donors”.
Next steps
The team now plans to adapt and test the model for heart and lung transplantation. If successful, this approach could transform prediction processes across multiple organ types, improving access for people waiting for donor organs and enhancing the efficiency of transplant systems worldwide.
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Digital and AI Strategies Emerge as Central to Expanding Health System Capacity, Survey Finds
Key Takeaways:
- Health system leaders increasingly view AI and digital health as essential to expanding capacity without adding buildings or clinical staff.
- Surveyed executives highlight persistent system pressures, including unaffordable care, limited access to primary care, and insufficient management of people’s health and wellbeing.
- Most leaders believe that fundamental operational change, underpinned by AI and digital tools, will be necessary to create sustainable, proactive models of care.
Introduction
A new report from the healthcare advisory firm Chartis suggests that digital health and artificial intelligence are now central pillars in health system leaders’ strategies to expand capacity, improve access, and operate more sustainably. The findings come from the firm’s fifth annual digital transformation survey, conducted in September 2025, which examined the perspectives of 150 health system executives on their progress and priorities in digital transformation.
Persistent pressures on healthcare delivery
The survey underscores the mounting pressures facing health systems today. Executives identified several entrenched challenges that continue to shape healthcare delivery:
- Unaffordable care was cited by 61 per cent of respondents as a major concern.
- Insufficient management of people’s long-term health and wellness was highlighted by 52 per cent.
- Limited timely access to primary care was reported by 49 per cent of leaders.
More than half of surveyed leaders believe that the sustainability of current care delivery models will decline further over the coming five years.
A shift from reactive to proactive care
In response to these pressures, there is widespread agreement that health systems must undergo fundamental change. According to the survey, nine in ten executives feel that organisations need to move away from reactive care and adopt more proactive, anticipatory models.
AI and digital health solutions are now widely considered critical to achieving this shift. The report notes that 90 per cent of leaders are already prioritising investments in digital and AI capabilities to support operational transformation.
AI and digital tools to expand capacity
Executives emphasised the importance of AI and digital health in increasing capacity while avoiding costly infrastructure or workforce expansion. Over the next five years, leaders expect these capabilities to be essential for serving more people without increasing physical space or clinical headcount.
Key priorities include:
- Freeing clinicians’ time for direct care through the use of AI (reported as very important by 52 per cent).
- Maximising access to clinical expertise using digital tools (51 per cent).
- Developing digitally enabled referral channels (45 per cent).
- Building hospital-at-home models as an alternative to inpatient care (36 per cent).
Expanding reach and access to care
Leaders also highlighted a strong need to extend the reach of healthcare services. More than half (53 per cent) stated that expanding delivery through initiatives such as care-at-home or mobile clinics is very important to improving access.
Several digital approaches were identified as particularly valuable for enhancing timely and convenient access:
- AI coaches to answer people’s questions (44 per cent).
- Connected devices and remote diagnostics to gather real-time health data (43 per cent).
- AI-enabled risk prediction to identify emerging health issues (43 per cent).
Supporting personalised patient journeys
Personalisation is another priority area, with leaders recognising the potential of digital platforms and AI to tailor the patient journey. The survey found:
- 52 per cent view offering multiple digital communication channels as very important for personalising the experience.
- 48 per cent believe that enhanced data collection and AI-supported analytics will be key to developing personalised care plans.
Call to action from Chartis
Tom Kiesau, co-author of the report and chief AI and digital officer at Chartis, emphasised the urgency of acting on these insights. He stated in the press release:
“Organisations need to capitalise on the momentum in this moment – and ensure that they are truly realising the potential presented by AI and digital capabilities to drive needed business transformation at scale.”
Ambient AI Helps 93% of Doctors Provide Patients with Their “Full Attention”, Sutter Health Study Shows
Key Takeaways:
- Ambient augmented intelligence significantly reduced after-hours documentation and cognitive burden among participating clinicians, with 93 percent reporting they could give patients their full attention.
- Burnout indicators improved, with self-reported after-hours note-taking falling sharply and overall stress scores declining following the pilot’s introduction.
- Despite early challenges around EHR integration and note customisation, clinicians expressed strong enthusiasm for continued use and future development of the technology.
Introduction: Tackling documentation burden with ambient AI
For many clinicians, the administrative workload associated with electronic health record (EHR) systems extends well into the evening, contributing to frustration, diminished work satisfaction and widespread burnout. At Sutter Health in California, leaders have undertaken a substantial effort to determine whether ambient augmented intelligence (AI) could help relieve this pressure and restore time and attention to patient care.
A recent pilot study, published in JAMA Network Open, involved physicians and non-physician providers across the organisation. Participants reported spending less time on after-hours notes, feeling more present with patients during consultations and experiencing early signs of reduced stress. Although limitations remain, the findings suggest that carefully implemented AI-supported documentation could contribute meaningfully to clinician well-being.
National data from the American Medical Association (AMA) illustrate the scale of the problem. Burnout rates among physicians peaked at 62.8 percent in 2021, before falling back to near-2011 levels by 2023. Clinicians remain 82 percent more likely to report burnout than workers in other fields, according to research published in Mayo Clinic Proceedings.
The documentation challenge: A core driver of burnout
The EHR has long been identified as a key contributor to rising workload. Prior research shows that clinicians are “spending two hours of desktop medicine documenting for every hour that they’re spending with patients,” noted Veena Jones, MD, a paediatrician and Sutter Health’s Chief Medical Information Officer, speaking at the 2025 American Conference on Physician Health in Boston.
Sutter Health is a member of the AMA Health System Member Program, which supports health systems with enterprise-level tools designed to strengthen leadership and improve the future of clinical care.
Dr Jones highlighted the cumulative impact of documentation on clinician well-being: “Another national survey showed that about 77 percent of physicians reported that these excessive documentation tasks were leading to longer clinic hours or the need to work from home. Those clinicians who indicated that they had a more favourable view and experience and were highly satisfied with the EHR were less likely to be burned out, which can suggest that changes made to the EHR, particularly through documentation, may be able to provide some relief to this.”
Pilot design: Bringing ambient AI to 100 clinicians
The pilot involved 100 clinicians across multiple specialties and eight medical groups in Northern and Central California. Leaders intentionally recruited a diverse cohort, including primary care clinicians, various specialty clinicians and informatics champions who could model the technology for peers.
Survey findings following the pilot demonstrated significant improvements:
- The proportion of clinicians reporting they spent one hour or less each week on after-hours notes rose from 14 percent to 54 percent.
- The percentage who felt able to give patients their full attention increased from 58 percent to 93 percent.
- Burnout scores dropped from 42 percent to 35 percent.
Cheryl Stults, PhD, senior scientist at the Sutter Health Centre for Health Systems Research, described reductions in cognitive burden: “Regarding task load and cognitive burden, all three of the measures – difficulty accomplishing note writing performance, having to complete notes at a hurried and rush pace, and just the overall mental demand from these tasks – decreased statistically significantly from the pre to the post period.”
The AMA continues to lead efforts to reduce administrative strain through targeted support and system reforms to help clinicians rediscover a greater sense of professional fulfilment.
Early limitations: Integration and customisation gaps
Despite promising outcomes, clinicians identified several challenges during the pilot. These included limited EHR integration, reduced freedom to customise note formats and gaps in specialty-specific templates for physical examinations.
Stults noted: “Despite all of the benefits, there were also some challenges and limitations that they noted from their experience with AI. When our pilot was launched back in April 2024, at the time it was not fully integrated into the EHR. Physicians either had to copy and paste into the EHR or do an additional step to incorporate it into that.”
Since the pilot, full EHR integration has been implemented, resolving one of the most significant issues.
Clinicians also wanted greater flexibility in document structure. As Stults explained: “Additionally, physicians were unhappy that they were unable to customise or format the progress note for future ones, so if they like their note formatted a certain way, they would have to do it every single time – they wanted a way for the AI to remember or to have a level of permanent customisation.” The inclusion of clinicians from a wide range of specialties was intentional, helping ensure templates could be refined more effectively over time.
Participants also sought further functionalities, such as greater accuracy in direct dictation and more precise word-for-word transcription.
Despite these limitations, enthusiasm remained high. As one clinician commented, “I’m very committed to making this work and I really believe that AI will be the way we chart in the future.”
The AMA’s broader work in digital health includes the recent launch of the AMA Centre for Digital Health and AI, designed to ensure clinicians have a strong voice in shaping the use of AI technologies in patient care.
Scaling responsibly: Support over mandates
Following full integration into the EHR, Sutter Health transitioned from the pilot phase to systemwide expansion. Clinicians opted in using a simple self-service form, and most were able to implement the technology after completing two short e-learning modules.
Dr Jones explained: “Part of the uncertainty of knowing how this would go drove us towards a staged monthly implementation where we had our physicians indicate interest with subsequent onboarding. Once we had full EHR integration, we began a self-enrolment process, which was a really simple form. If anyone wants it, they go to our site, they sign up and within a month they will be provisioned.”
Training was streamlined as well. Early analysis suggested that more than two-thirds of clinicians felt confident going live without intensive support. As a result, Sutter Health created a self-guided e-learning module consisting of two seven-minute videos.
Clinical champions remained available to provide at-the-elbow guidance, while a digital academy support team carried out follow-up and troubleshooting.
The AMA’s STEPS Forward webinar, “AI Tools for Documentation: The Newest Member of the Care Team,” provides further insight into how ambient AI can support clinicians and improve care delivery.
Monitoring use and supporting adoption
Sutter Health monitors engagement through monthly utilisation reports. Dr Jones described the organisation’s proactive outreach strategy: “We run monthly reports looking at utilisation and have the team do targeted outreach to those who are not using it to say: Hey, can we help you? And if not, we actually go through a licence repurposing programme.”
This targeted support has helped increase adoption considerably. In March, Sutter Health also became the first organisation to launch a fully integrated inpatient workflow with its ambient AI vendor. This decision came only after the integrated tools demonstrated sufficient maturity to support hospital-based documentation.
As of September, the organisation has been extending the self-service enrolment model across hospitals and emergency departments (EDs).
The shift in clinician demand has been striking. As Dr Jones observed, the usual dynamic of “pushing” new technology has shifted towards clinicians actively requesting access: “The pull versus push has been incredible. In my career, this is one of the most exciting things to be a part of because physicians are pulling for it, and they want it. We have over 1.2 million notes written and that’s increasing at 50,000 a week.”
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AI-Supported Telehealth Enhances Hip Surgery Recovery in Regional Australia
Key Takeaways:
- A co-designed, digitally supported recovery pathway is helping people in regional Australia access surgeon-approved post-operative hip care without the need for long-distance travel.
- The Panacea Pathway integrates predictive analytics and AI-generated insights to monitor progress, identify risks early, and support safer and more consistent recovery at home.
- More than 3,000 at-home clinical appointments have been delivered, with patients reporting greater confidence, improved continuity of care, and fewer missed appointments.
Addressing gaps in regional post-operative care
Hip replacement surgery is among Australia’s most frequently performed orthopaedic operations, and the demand for this intervention continues to grow as the population becomes older. However, people living in regional areas remain disproportionately affected by barriers to appropriate post-operative follow-up. Many must travel long distances to attend surgeon or specialist reviews, face reduced access to multidisciplinary care, and often lack the reassurance that accompanies regular in-person monitoring.
Recognising these persistent challenges, the Fortius Institute for Musculoskeletal Research (FIMR), working in partnership with the Sunshine Coast Orthopaedic Group, identified an opportunity to reshape the recovery experience. Together, they developed the Panacea Pathway: a digitally supported, surgeon-approved rapid recovery pathway delivered directly into people’s homes by nurse practitioners through a Nurse Concierge model.
Co-designing a digital recovery service
To turn this concept into a structured clinical pathway, FIMR partnered with the University of the Sunshine Coast to design the Nurse Concierge service specifically for people recovering from hip arthroplasty. This collaboration was supported by the Queensland Government’s Regional University Industry Collaboration (RUIC) programme, delivered by CSIRO, which connects regional universities with small and medium-sized enterprises to facilitate research partnerships across Queensland.
For the Sunshine Coast Orthopaedic Group, the RUIC partnership offered access to advanced research expertise and data capabilities that would not have been available independently. With direct support from the University of the Sunshine Coast, the team was able to gather a broader and more detailed dataset, providing a stronger foundation for training AI tools using real-world clinical information.
This work resulted in a clinical care pathway combining surgeon-approved best practice with at-home delivery by nurse practitioners. The approach integrates predictive analytics and AI-driven insights to remotely monitor each person’s progress, detect early signs of risk, and support timely intervention. In doing so, it reduces the physical and practical burden on patients while promoting safer and more consistent recovery outcomes.
“Seeing our research directly improve patients’ lives has been incredibly rewarding, and this project is demonstrating our approach is leading to safer, better and faster recovery for patients undergoing major orthopaedic surgery,” said Professor Nick Ralph from the University of Sunshine Coast.
He added: “Working alongside clinicians through the RUIC partnership meant we could refine our data collection methods in real-time, building the robust dataset needed to develop an evidence-based care pathway.”
Impact and future direction
Early feedback on the at-home Nurse Concierge service has been highly positive. More than 3,000 at-home clinical appointments have already been completed, substantially reducing travel time for regional participants and resulting in fewer missed follow-up appointments. Many patients reported feeling more confident in their recovery journey, emphasising the value of personalised monitoring and consistent contact with the same nurse practitioner.
“This project has demonstrated how remote care and digital health tools can reimagine the patient journey,” said Dr Stephanie Chaousis, Head of Digital Innovation at FIMR.
“By combining clinical expertise with data-driven insights, we’re not just improving recovery outcomes – we’re establishing a new standard for musculoskeletal care.”
The extensive dataset collected through the programme is now informing further enhancements to the pathway for joint replacement patients. Building on the success of the hip surgery pilot, the Sunshine Coast Orthopaedic Group intends to expand the Panacea Pathway to include knee replacement procedures.
With its strong foundation in real-world data, interdisciplinary collaboration, and AI-enabled monitoring, the Panacea Pathway offers a scalable model that could be adopted across hospital networks. Its principles have the potential to inform future clinical guidelines and broaden access to safer, more equitable recovery pathways for people throughout Australia.
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Digital Tools Show Promise in Schizophrenia Care, Large-Scale Study Finds
Key Takeaways:
- A smartphone app, FOCUS, helped people living with schizophrenia manage symptoms and recovery more effectively.
- External facilitation by digital specialists improved outcomes, including reduced psychiatric emergency visits.
- Researchers highlight that the greatest challenge for digital mental health tools remains real-world adoption.
Evidence grows for mobile mental health support
A major clinical study has found that using a smartphone app to support the treatment of schizophrenia can deliver modest but meaningful improvements in symptoms and recovery outcomes. The research, published in Psychiatric Services, is among the largest trials of its kind examining the impact of digital interventions on people with serious mental illness.
While the research took place in the United States, its findings are relevant globally as health systems, including the NHS, seek innovative ways to expand access to mental healthcare and support self-management outside the clinic.
The FOCUS app: bridging the gap between appointments
The FOCUS app was first launched in 2013 to provide digital support for people living with schizophrenia and related conditions. It offers structured prompts and tools to help users manage symptoms, take medication consistently, improve sleep routines, and practise social and coping skills.
Previous small-scale studies showed early promise, but uptake within mental health services has been limited, reflecting wider challenges in embedding digital tools into routine care.
Comparing models of implementation
The new trial enrolled 274 people receiving care for schizophrenia across 23 community clinics. It tested two models of introducing the FOCUS app into clinical practice:
- External facilitators – digital health specialists who supported multiple clinics and provided guidance to both staff and patients.
- Internal facilitators – trained in-house staff who integrated app use within their own teams and services.
Both methods proved feasible, but patients supported by external facilitators experienced better clinical outcomes, including fewer psychiatric emergency attendances.
Digital tools and the challenge of adoption
“Getting access to a mental health provider can be challenging for patients with serious mental illness,” said Dr Dror Ben-Zeev, clinical psychologist, director of UW Medicine’s BRiTE Center, and the study’s lead author. “Mobile health tools hold enormous potential because we can meet patients where they are. The biggest hurdle today for digital mental health solutions is effective implementation and real-world adoption.”
Each participant in the study was paired with a digital navigator, who had access to the individual’s app data, conducted weekly check-in calls, and communicated key updates to the person’s clinical team.
Ensuring digital solutions deliver real results
“There are so many digital solutions being offered right now,” commented Dr Charissa Fotinos, Washington State Medicaid and behavioural health medical director, who served as an advisor on the project. “It’s important for us, as a payor, to support tools that have evidence of efficacy. We need to pay for things that work.”
Her remarks echo a growing sentiment among UK health commissioners and policymakers: while digital mental health tools hold promise, investment must prioritise those that have been proven to work in practice, not just in theory.
Towards a future of evidence-based digital psychiatry
The study forms part of mHealth Washington, a multi-year programme funded by the National Institute of Mental Health, developed in collaboration with the University of Washington School of Medicine and the Washington State Health Care Authority.
Researchers emphasised the wider relevance of their findings to healthcare systems internationally. As the NHS continues to expand its use of digital therapy tools, from remote cognitive behavioural therapy to AI-assisted triage, evidence such as this may help shape best practice for integrating digital interventions into routine psychiatric care.
Conflict-of-interest statements for the study authors are available in the published paper.
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Digital Health Programme Boosts Lung Cancer Screening Uptake in High-Risk Individuals
Key Takeaways:
- A digital health intervention (mPATH-Lung) increased lung cancer screening rates by over 40% compared with usual care.
- The programme helped people overcome common barriers to screening, including lack of awareness and limited clinical consultation time.
- Findings demonstrate the potential of direct-to-patient digital tools to improve early cancer detection and preventive care.
Digital tools for early detection
A new study led by researchers at Wake Forest University School of Medicine, in collaboration with the University of North Carolina at Chapel Hill and MD Anderson Cancer Center, has shown that a direct-to-patient digital health programme can significantly increase lung cancer screening rates among people at high risk.
The findings were published in JAMA and mark an important step towards using digital health to support early cancer detection.
Lung cancer remains the leading cause of cancer-related death worldwide. However, early detection through low-dose computed tomography (CT) screening can dramatically improve outcomes and survival rates. Despite this, fewer than 20% of eligible individuals in the United States currently undergo lung cancer screening each year.
Common barriers include a lack of awareness, confusion over screening eligibility guidelines, and limited opportunities for shared decision-making within standard clinical appointments.
“Our goal was to address these barriers by testing a digital programme that reaches patients directly, outside of traditional clinical encounters,” said David P. Miller, M.D., Professor of Implementation Science in the Division of Public Health Sciences at Wake Forest University School of Medicine and corresponding author of the study.
How the study worked
The randomised clinical trial was conducted across two major academic health systems in North Carolina. More than 26,000 individuals with a history of smoking were invited to take part. Those who met the screening eligibility criteria were randomly assigned to one of two groups: the mPATH-Lung digital health programme or enhanced usual care.
The enhanced usual care group received a message informing them that they were eligible for lung cancer screening and were encouraged to speak with their primary care clinician. They also viewed a short educational video on lung health. Although this provided more support than standard practice, it did not include access to the mPATH-Lung platform.
By contrast, participants in the mPATH-Lung group received access to a fully digital intervention comprising:
- A brief educational video,
- A structured decision aid outlining the benefits and risks of screening, and
- An option to request a screening appointment directly online.
This approach allowed participants to learn at their own pace and make informed decisions without needing an in-person consultation.
The main outcome measured was the completion of a low-dose CT scan for lung cancer screening within 16 weeks of enrolment.
Results and key findings
The results demonstrated a clear improvement in screening uptake.
- 24.5% of participants who used the mPATH-Lung programme completed a screening CT scan, compared with 17% of those in the enhanced usual care group.
- The increase in screening rates was consistent across demographic and socioeconomic groups, suggesting that the digital approach may help reduce health disparities.
- There were no reported complications from screening-related procedures in either group.
“Our study shows that reaching patients directly with digital tools can help overcome barriers to lung cancer screening and potentially save lives,” said Miller. “By empowering individuals with information and easy access to screening, we can make a real difference in early detection of lung cancer.”
Implications for preventive health
According to Miller, the findings highlight that digital health interventions can modestly but meaningfully improve screening uptake, even among populations that have historically faced barriers to preventive care. Early detection is crucial, as individuals diagnosed at an early stage of lung cancer have significantly higher survival rates.
The research team believes that the mPATH-Lung approach could be adapted to other preventive health services, enabling more people to benefit from life-saving interventions such as cancer screenings, vaccinations, and chronic disease management programmes.
Next steps and future research
The researchers emphasised that further studies are needed to evaluate digital lung cancer screening initiatives across a broader range of healthcare settings and populations. They also plan to explore strategies for maintaining patient engagement with digital health tools over time.
To extend the impact of their work, Miller and Ajay Dharod, M.D., Associate Professor of Internal Medicine at Wake Forest University School of Medicine, have co-founded mPATH Health – a startup designed to make the programme widely available. The venture aims to expand access to lung cancer screening and other forms of preventive care, aligning with Advocate Health’s academic learning health system model, which focuses on translating research into real-world, scalable solutions.
Miller, Dharod, and Wake Forest University Health Sciences hold ownership interests in the mPATH technology used in the research.
Funding and acknowledgements
This research was supported by the National Cancer Institute under grant R01CA237240. The project utilised the Data and Design Services of the Wake Forest Clinical and Translational Science Institute, supported by the National Center for Advancing Translational Sciences (NCATS) through award UM1TR004929. Additional funding was provided by the University Cancer Research Fund of the University of North Carolina at Chapel Hill Lineberger Comprehensive Cancer Center.
The project also benefited from services provided by the North Carolina Translational and Clinical Sciences Institute, funded by NCATS through award UM1TR004406.
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American Medical Association Establishes Centre for Digital Health and AI to Place Physicians at the Forefront of Technological Innovation
Key Takeaways:
- The American Medical Association (AMA) has launched the Centre for Digital Health and AI to ensure physicians play a central role in shaping and integrating emerging technologies in medicine.
- The Centre will focus on policy leadership, clinical workflow integration, education, and collaboration to guide the responsible use of digital and AI tools in healthcare.
- The initiative aims to bridge enthusiasm for AI among physicians with practical strategies that safeguard data privacy, reliability, and patient-centred outcomes.
A new centre to shape the future of digital health
The American Medical Association (AMA) has announced the launch of its Centre for Digital Health and AI, an initiative designed to position physicians at the heart of digital transformation in healthcare. The Centre aims to guide the development, implementation, and regulation of technologies such as artificial intelligence (AI), ensuring they serve both clinicians and patients effectively.
While digital health tools and AI systems are progressing at an unprecedented pace, their potential can only be fully realised when developed with clinical insight. Without physician involvement, these technologies risk introducing new administrative burdens and failing to integrate meaningfully into healthcare practice.
By embedding physicians throughout the entire technology lifecycle – from concept to deployment – the AMA seeks to ensure innovations enhance clinical workflows, reduce friction in practice, and ultimately improve patient outcomes.
Physician leadership in the age of AI
“Augmented Intelligence will be a defining force in the future of health care, but right now we are barely scratching the surface of its potential. Digital health tools are everywhere and the technology has limitless opportunity, but if you don’t understand clinical practice or clinical workflow, even the best tools will never be fully implemented,” said John Whyte, MD, MPH, CEO and Executive Vice President of the AMA.
“By launching this Centre, the AMA is leading in this space so physicians have a say in the technology and clinical care of the future. Our goal is to harness innovation responsibly and effectively, so it improves patient care and reduces unnecessary burdens on physicians,” he added.
Dr Whyte’s comments highlight a key challenge within healthcare innovation: ensuring that technological progress aligns with the realities of clinical practice. The AMA’s new Centre seeks to act as both a bridge and a safeguard – connecting the rapid pace of technological development with the values and needs of medical professionals and their patients.
Key areas of focus
The Centre for Digital Health and AI will concentrate on four principal areas:
1. Policy and Regulatory Leadership
The Centre will collaborate with regulators, policymakers, and technology leaders to develop benchmarks and guidance for the safe and effective use of AI and digital health tools. This includes contributing to policy discussions around data protection, algorithmic transparency, and equitable access to digital innovation.
2. Clinical Workflow Integration
Recognising that even the most advanced tools can fail without proper clinical fit, the Centre will create opportunities for doctors to inform how AI and digital tools are designed. The goal is to ensure technologies enhance both clinician and patient experience by supporting efficiency and accuracy in clinical decision-making.
3. Education and Training
The AMA will equip physicians and health systems with the skills and knowledge needed to adopt AI responsibly. Training programmes will help clinicians understand how to interpret AI outputs, evaluate new technologies, and integrate them seamlessly into everyday practice.
4. Collaboration and Partnership
The Centre will foster partnerships across the technology, research, government, and healthcare sectors to encourage innovation that aligns with patient needs and ethical standards. This collaborative approach aims to ensure that AI applications in medicine remain grounded in clinical realities and societal priorities.
Balancing enthusiasm and caution
Recent AMA surveys reveal growing physician enthusiasm for AI’s potential in medicine. Approximately two-thirds of physicians have already incorporated AI-enabled tools into some aspect of their practice, demonstrating the rapid pace of adoption. However, the same surveys show that one in four physicians remains more concerned than excited, citing ongoing issues around data privacy, reliability, and patient safety.
The AMA’s Centre for Digital Health and AI intends to bridge this divide – helping physicians feel confident in leveraging AI while addressing legitimate concerns. By guiding ethical integration and promoting education, the AMA hopes to create a healthcare environment where digital innovation enhances both care quality and professional satisfaction.
A step towards responsible innovation
As digital transformation reshapes the healthcare landscape, the AMA’s initiative underscores the importance of responsible innovation led by clinical expertise. The Centre for Digital Health and AI represents a strategic step toward embedding physician insight into every stage of technological development – ensuring that the promise of AI and digital tools translates into real-world improvements for patients and practitioners alike.
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New Study Tests Virtual Mindfulness Therapy to Ease Stress in Young People Living with Diabetes
Key Takeaways:
- A three-year, $941,418 NIH-funded study will assess whether virtual reality–enhanced mindfulness can reduce stress in young people living with type 1 diabetes.
- Researchers from Wayne State University and Johns Hopkins University aim to improve coping and mental health outcomes through immersive, accessible virtual sessions.
- If effective, the intervention could be scaled to benefit other young adults with chronic conditions and high stress levels.
Exploring virtual reality for stress reduction
Researchers from Wayne State University and Johns Hopkins University are investigating how virtual reality (VR) might help young adults living with type 1 diabetes better manage stress. The study, titled “Feasibility of MBSR-VR to Reduce Stress among Emerging Adults with T1D,” is supported by a three-year grant of $941,418 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health (NIH).
April Idalski Carcone, Ph.D., Professor of Family Medicine and Public Health Sciences at Wayne State University’s School of Medicine, serves as co-principal investigator on the project alongside Dr Erica Sibinga, M.D., M.H.S., Associate Professor of Paediatrics at the Johns Hopkins University School of Medicine.
The impact of stress on young people with diabetes
Dr Carcone explained the importance of the research:
“We’ve been collaborating with Johns Hopkins University on this line of research for more than 10 years. Diabetes is a chronic illness that creates additional stress in young people who are already going through a lot of anxiety figuring out their lives and deciding what to do after high school and so forth.”
She noted that stress can significantly worsen physical health:
“Stress can exacerbate health issues, particularly for those already going through physical challenges. Cortisol increases as a result of stress, and stress can essentially wear out the body. So if your body is already going through difficulties, it can make your health even worse.”
Young people living with type 1 diabetes must manage demanding self-care routines and fluctuating glucose levels, often while navigating major life transitions. These pressures contribute to a higher risk of anxiety, depression, and burnout.
Mindfulness meets virtual reality
The research team aims to evaluate the feasibility and acceptability of delivering Mindfulness-Based Stress Reduction (MBSR) through virtual reality, referred to as MBSR-VR. The approach integrates traditional mindfulness practices with immersive VR environments designed to foster relaxation and focus.
Dr Carcone said:
“One of the challenges we had with an earlier version of this research was that we were gathering people onto campus for group intervention sessions, but it was logistically difficult to bring everyone to campus at the same time in the same place. Instead, we decided to try this in a virtual format.”
She added that the virtual environment offers greater engagement and flexibility:
“People coming together in a VR space sounded very exciting and provided us with a format that was a little more engaging. We can utilise different virtual environments as opposed to the split-screen Zoom-style call that we are all so familiar with. You can virtually gather people around a campfire, in a pool where you can toss a virtual beachball around, and so forth.”
Research collaboration and goals
Alongside Dr Carcone and Dr Sibinga, the project includes Dr Deborah Ellis, Associate Department Chair of Research for the Department of Family Medicine and Public Health Sciences at Wayne State University, and Dr Angulique Outlaw, Associate Professor of Behavioural Sciences within the same department.
The study will explore whether MBSR-VR can:
- Improve coping mechanisms for stress among individuals aged 16–20 with type 1 diabetes and high stress reactivity.
- Enhance mindfulness and emotional well-being.
- Positively influence glycaemic control and reduce psychological distress, including symptoms of depression and anxiety.
If successful, the intervention could be adapted for broader use across other chronic conditions where stress significantly impacts health outcomes.
Reaching young adults where they are
Dr Carcone highlighted how the virtual approach could make mindfulness training more accessible and socially engaging:
“Youths between ages 16 and 20 are very motivated by their social life, peers and significant others. These techniques allow us to bring people together who might not otherwise be able to come together.”
She emphasised that the programme could reach those living in rural or remote areas:
“In Detroit, you can gather patients at a hospital, but this method will also allow us to help those living in more rural communities. There’s often not another person who has type 1 diabetes if you live in a small Upper Peninsula community, for instance. This will let them touch base with others their own age who are going through something similar and share experiences that they might not be comfortable talking about with a friend who isn’t going through the same thing.”
Supporting research innovation
Ezemenari M. Obasi, Ph.D., Vice President for Research & Innovation at Wayne State University, praised the project:
“This award from the National Institutes of Health is an excellent example of the important research that our faculty are engaged in that are seeking solutions for complex challenges. The work of Dr Carcone and her collaborators could assist the lives of countless young people in Detroit, across Michigan and around the globe.”
Looking ahead
With stress recognised as a major barrier to effective diabetes management, this study may pave the way for new digital mental health interventions that combine accessibility, engagement, and clinical impact. Should MBSR-VR prove feasible and effective, it could form part of a new generation of evidence-based tools that empower young adults with chronic conditions to manage stress and improve their overall health and well-being.
Grant number: 1R01DK141816 (National Institute of Diabetes and Digestive and Kidney Diseases, NIH)
AI Model Could One Day Help Prevent Childhood Obesity by Counting Bites
Key Takeaways:
- Researchers at Penn State have developed an artificial intelligence (AI) system capable of counting how many bites a child takes during a meal, achieving around 70% accuracy compared to human observers.
- Eating too quickly increases the risk of obesity in children because the body has less time to register fullness, leading to overeating.
- The AI system, named ByteTrack, may in future help parents, clinicians, and researchers monitor and guide children’s eating habits in real-world environments.
AI and eating behaviours: A new frontier in obesity prevention
The faster a child eats, the greater their risk of developing obesity, according to researchers from the Penn State Department of Nutritional Sciences. However, accurately measuring bite rate—the number of bites taken during a meal—has long posed a challenge. Traditionally, this requires a researcher to watch and manually record each bite from hours of video footage, limiting most studies to small, controlled laboratory environments.
In a collaborative effort between Penn State’s Departments of Nutritional Sciences and Human Development and Family Studies, researchers have created an AI model designed to automate this process. Their pilot study, published in Frontiers in Nutrition, shows that the system is currently around 70% as effective as a human observer in counting bites. Although still under development, the researchers believe the technology could eventually help identify when a child needs to slow their eating rate or adjust their eating behaviour.
The link between eating speed and obesity
“When we eat quickly, we do not give our digestive tract time to sense the calories,” explained Professor Kathleen Keller, the Helen A. Guthrie Chair of Nutritional Sciences at Penn State and co-author of the study. “The faster you eat, the faster it goes through your stomach, and the body cannot release hormones in time to let you know you are full. Later, you may feel like you have overeaten, but when this behaviour repeats, faster eaters are at greater risk for developing obesity.”
Keller’s research group has previously demonstrated that a faster bite rate, especially when combined with larger bite size, correlates with a higher likelihood of obesity in children. Other studies have also linked larger bite size to an increased risk of choking.
“Bite rate is often the target behaviour for interventions aimed at slowing eating rate,” noted Dr Alaina Pearce, research data management librarian at Penn State and co-author of the study. “This is because bite rate is a stable characteristic of children’s eating style that can be targeted to reduce their eating rate, intake, and ultimately risk for obesity.”
Manually recording bite rate, however, is both labour-intensive and costly. As Keller pointed out, “Measuring bite rate is tedious, labour-intensive work, meaning it is expensive, which often limits the amount of data considered in bite rate studies.”
Using AI to support healthier habits
To overcome these limitations, Yashaswini Bhat, a doctoral candidate in nutritional sciences and lead author of the study, set out to develop the first AI-powered bite counter designed specifically for studying children’s eating behaviours.
“I have an interest in AI and data science, but I had never developed a system like this one,” Bhat explained.
She partnered with Associate Professor Timothy Brick, from Penn State’s Department of Human Development and Family Studies, to create a system capable of detecting children’s faces within videos and identifying when a child takes a bite.
“An experienced and knowledgeable collaborator like Dr Brick was invaluable to this project,” Bhat added.
The team trained the system using 1,440 minutes of video footage from Keller’s Food and Brain Study, funded by the National Institute of Diabetes and Digestive and Kidney Diseases. The footage featured 94 children aged seven to nine, each consuming four meals with identical foods on different occasions.
Researchers manually identified bites in 242 videos to train the AI. Once the system had been trained to recognise what a bite looks like, it was tested on an additional 51 videos. The AI’s results were then compared to those of human researchers.
Promising early results
“The system we developed was very successful at identifying the children’s faces,” Bhat said. “It also did an excellent job identifying bites when it had a clear, unobstructed view of a child’s face.”
While the AI was 97% as effective as a human observer at recognising faces, it achieved about 70% accuracy in counting bites. Bhat noted that challenges arose when children were partially obscured, turned away from the camera, or engaged in behaviours such as chewing on their spoons or playing with their food—actions common among younger participants.
“The system was less accurate when a child’s face was not in full view of the camera or when a child chewed on their spoon or played with their food, as often happens toward the end of a meal,” Bhat said. “Chewing on a utensil sometimes appeared to be a bite, and this complicated the task for the AI model.”
Next steps for the ByteTrack system
Although still in its early stages, the researchers view the pilot as an important step toward automating bite rate analysis. The system, called ByteTrack, will continue to be refined so it can distinguish between bites and similar movements such as sipping a drink.
“The eventual goal is to develop a robust system that can function in the real world,” Bhat said. “One day, we might be able to offer a smartphone app that warns children when they need to slow their eating so they can develop healthy habits that last a lifetime.”
The research was supported by the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of General Medical Sciences, the Penn State Institute for Computational and Data Sciences, and the Penn State Clinical and Translational Science Institute.
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