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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AI Tools Could Help Identify the Best Ways to Help Young People Quit Vaping
Key Takeaways:
- Researchers at the University at Buffalo used machine learning and explainable AI tools to identify which vaping cessation strategies may work best for different individuals.
- The study found that starting to vape before age 18 – especially before age 15 – was one of the strongest predictors of continued nicotine use.
- Researchers believe AI-driven approaches could help universities and public health teams move from generic stop-vaping programmes to more personalised interventions.
Understanding why young people struggle to quit vaping
Young adults between the ages of 18 and 24 are now among the heaviest users of e-cigarettes in the United States, with 38.4% of young people reporting habitual vaping. Rates of e-cigarette use are particularly high in Western New York, where vaping prevalence exceeds that seen in New York City.
Although awareness of the potential health risks associated with vaping has increased, many people still find it difficult to stop using e-cigarettes. Researchers say this challenge can be even greater for younger individuals, whose brains may be more susceptible to nicotine dependence.
These concerns prompted cancer researchers at the University at Buffalo (UB) to investigate why some young people continue vaping while others successfully quit. Their goal was not only to better understand vaping behaviour, but also to identify which cessation strategies may be most effective for different individuals.
The team conducted an online survey involving 119 people who vape, approximately three quarters of whom were aged between 21 and 26 years old. Their findings were published in PLOS Digital Health.
The senior corresponding author of the study was Supriya D. Mahajan, Ph.D., associate professor of medicine in the Jacobs School of Medicine and Biomedical Sciences at UB.
Researchers explore better ways to support vaping cessation
The study was driven by the researchers’ experiences treating people with nicotine dependence in clinical settings.
“As cancer researchers in the divisions of Hematology/Oncology and Allergy, Immunology, and Rheumatology at UB, we see the direct clinical consequences of nicotine dependence in our patients,” says Satheeshkumar Poolakkad Sankaran, DDS, first author of the study and research scientist in the Division of Hematology/Oncology in the Department of Medicine.
“We wanted to understand not only who is vaping but also who is successfully quitting—and then translate those insights into better cessation support for our cancer patients and into broader social determinants of health research.”
To investigate this, the researchers applied artificial intelligence techniques, including machine learning, to determine why some stop-vaping strategies appear to work for certain people but not for others.
The researchers noted that these findings could potentially extend beyond vaping cessation and inform wider public health approaches.
Using AI to predict who may successfully quit
The research team tested five different computer models designed to predict which individuals were most likely to successfully stop vaping.
According to Poolakkad Sankaran, some of the most effective models were also among the simplest.
“The simplest and most reliable ones were like a smart checklist that automatically figured out which life factors mattered most in deciding whether or not to stop vaping,” says Poolakkad Sankaran.
The researchers also explored more advanced forms of “explainable AI” – systems designed to help humans understand how AI reaches its conclusions.
Explainable AI offers insight into individual barriers
One explainable AI model used in the study was called Accumulated Local Effects (ALE). According to the researchers, this tool helps visualise how specific factors influence vaping cessation outcomes across larger groups of people.
“For example, the model called Accumulated Local Effects (ALE) shows how each factor—for example, being under age 21—changes the odds of quitting across the whole group, almost like a graph of ‘what-if’ scenarios,” says Poolakkad Sankaran.
The team also used another explainable AI approach known as Local Interpretable Model-Agnostic Explanations (LIME), which focuses on individuals rather than groups.
“Another model, Local Interpretable Model-Agnostic Explanations (LIME), zooms in on individual people,” says Poolakkad Sankaran. “It can look at one specific vaper and say, ‘For this person, social triggers are the biggest barrier—here’s exactly how much they lower their chance of success.’”
Researchers believe these tools could eventually help clinicians and counsellors provide more personalised support rather than relying on standardised approaches for everyone.
Earlier vaping initiation linked to greater difficulty quitting
One of the clearest findings from the study was the strong relationship between early vaping initiation and continued nicotine use later in life.
The researchers found that individuals who began vaping before the age of 18 – particularly before age 15 – were significantly more likely to continue vaping and struggle with cessation.
“Starting before age 18, and especially before 15, was one of the strongest predictors of continued use,” says Poolakkad Sankaran.
“This tells us that prevention must begin early, before the brain’s reward system becomes wired to nicotine. For kids who already started young, the message is hopeful but urgent: The sooner they get help, the better their chances.”
The researchers suggested that vaping cessation programmes aimed at younger individuals should be specifically tailored to this age group.
“These should be age-tailored strategies: short, frequent digital nudges; peer support; and trigger-management tools, because their developing brains make these people especially vulnerable but also especially responsive to timely intervention,” he explains.
Universities could play a key role in vaping prevention
Poolakkad Sankaran believes universities could become important centres for implementing AI-driven vaping cessation support.
“UB is uniquely positioned to translate these exploratory machine learning/explainable AI results into real-world programs that reduce nicotine addiction, lower long-term health care costs and address health disparities affecting Buffalo’s young population,” he says.
The researchers suggest that university health services and local public health departments could use these findings to build personalised text-message campaigns targeting the most common triggers associated with vaping in local communities.
They also propose that campus applications or quit-support services could identify students at higher risk – including younger individuals, frequent users and people vulnerable to social vaping triggers – and provide immediate tailored support.
AI tools could be integrated into existing stop-vaping programmes
The research team now plans to integrate their predictive models into digital vaping cessation tools, including expanded versions of existing programmes such as “This is Quitting.”
The aim is to help counsellors better understand why a particular student may be struggling and which intervention strategies are most likely to succeed.
“Because the study was done locally with Western New York participants, the findings already reflect the realities our students and young adults face,” says Poolakkad Sankaran.
The researchers say the work highlights how predictive analytics and machine learning could help public health professionals move beyond one-size-fits-all approaches.
He adds that the research demonstrates how machine learning and predictive analytics can help public health teams move from one-size-fits-all programs to precision interventions by identifying who is most at risk and what will actually help them before they drop out of treatment.
“Our study pushes the field forward by showing that explainable AI (XAI) can make these powerful tools transparent and trustworthy for clinicians and policymakers,” he says.
“Instead of a black-box prediction, we deliver actionable, human-understandable explanations that can be directly built into digital health apps and community programs.”
Source: Medical Xpress
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