Background: Early mobilization and mobility are essential components of the recovery process following surgery and trauma-related hospitalization. In addition to personalized support from physiotherapists and health care professionals, assistive devices such as walkers play a crucial role in facilitating safe and effective mobility. Objective: This scoping review aims to provide a comprehensive overview of the current state of the literature on the design, sensor technologies, and functional applications of smart walkers and to assess the extent to which existing studies reflect clinical use cases. Methods: Peer-reviewed English articles published between 2015 and 2024 were identified by searching PubMed, CINAHL, SSCI, and IEEE, focusing on the topic of smart walkers. Secondary analyses and walkers with 2 wheels or fewer were excluded in abstract screening. Study screening and selection were performed according to the Joanna Briggs Institute guidelines for scoping research and reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The Rayyan systematic review management software was used for study selection. The articles included were analyzed with respect to the sensor technologies used, their functional capabilities, and their application scenarios. Results: Of the 800 articles screened, 44 (5.5%) met the inclusion criteria. Most of these articles were research reports (n=36, 81.8%) and were conducted in laboratory-based environments (n=30, 68.2%). Most studies evaluated smart walkers in asymptomatic populations (n=29, 65.9%), with half (n=22, 50%) involving younger adults. Among the sensor modalities reported, camera-based and light detection and ranging–based sensors were most prevalent for half of the implementations. Light detection and ranging–based sensors can be categorized according to their primary functions: gait analysis (n=11, 25%), collision detection (n=9, 36%), and navigation (n=5, 11.4%). Load sensors (n=10, 22.7%) and ultrasonic sensors (n=11, 25%) were among the most frequently cited sensor modalities in the literature. Load sensors, also known as force sensors, are integrated into the handlebars, frame, forearm supports, or chest pads of smart walkers. These sensors measure the user’s load, providing essential data for calculating body weight support or inferring the user’s intention to move. Conclusions: The smart walkers described in the literature were predominantly tested in asymptomatic and younger populations. Bridging the gap between current laboratory-based research and real-world clinical environments, as well as the daily lives of end users, remains a critical objective. Addressing the specific needs of older adults through comprehensive requirements analyses and iterative testing continues to be an ongoing challenge, yet these processes can serve as integral components of research and development projects. Trial Registration: OSF Registries osf.io/ctpf4; https://osf.io/ctpf4
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New Study Identifies Different Biological Subtypes of Autism
Research findings help explain why symptoms present so differently from one child to the next, and why individualized supports and interventions are essential.
Autism can look very different from person to person. One child might differ from another in how they learn, process sensory information, and experience social and communication challenges. Scientists have long suspected these differences stem from distinct biology, but proving it has been challenging — until now.
A recent study published in Nature Neuroscience has identified two biological subtypes of autism linked to different pathways in the brain.
Researchers from the Child Mind Institute, the Istituto Italiano di Tecnologia, and other international partners analyzed brain connection patterns in nearly 2,000 individuals, including 940 autistic people from the Autism Brain Imaging Data Exchange (ABIDE). By combining human brain-imaging datasets with complementary biological data, they identified two consistent patterns in how different brain regions communicate.
One subtype showed reduced communication, or hypoconnectivity, among brain regions linked to pathways that help brain cells send signals to one another. The other showed increased communication, or hyperconnectivity, among brain regions linked to pathways associated with the immune system. The two subtypes exhibited differences in functional brain structure and modest differences on standardized autism assessments, with the hyperconnectivity subtype scoring moderately higher on autism severity measures.
These findings give scientists the first empirically biology-based framework for understanding autism’s complexities over time. This type of work could move the field closer to more precise, personalized approaches to medicine and care. However, this does not mean autism can now be divided into just two categories, nor does it create a new diagnostic framework. Autism is complex, and these two subtypes are likely part of a much larger picture.
The study also highlights the importance of open science. Through shared datasets like ABIDE, researchers can tackle questions too large for a single lab to answer alone.
The post New Study Identifies Different Biological Subtypes of Autism appeared first on Child Mind Institute.
STAT+: Praise for FDA’s acting commissioner
RFK Jr. adviser Calley Means and Kennedy’s son Finn attended the Enhanced Games, the pro-doping athletic competition and biohacking extravaganza that took place over the weekend in Las Vegas, according to The Washington Post. Send news tips and personal bests to John.Wilkerson@statnews.com or John_Wilkerson.07 on Signal.
Ripple effects
For weeks, Republicans have been preoccupied with an immigration funding bill that they’re pushing through Congress, without support from Democrats. I’ve not been writing about that bill because it doesn’t include health care policies. But it’s now becoming relevant to health care, albeit indirectly.
Early last week, Republicans were expected to pass that budget reconciliation bill without much friction. By the end of the week, Senate Republicans adjourned for a week-long recess without voting on it due to an impasse over a new $1.8 billion settlement fund for Trump’s allies. They’d also butted heads with the president over his demands for $1 billion for a White House complex and ballroom.
Telehealth by Home Monitoring and Video Consultation for Children With Cystic Fibrosis: Qualitative and Quantitative Study
Background: Clinical outcomes for patients with cystic fibrosis have improved over the last decades, with a focus on enhancing the quality of life for children and their families. These improvements are expected to reduce the need for in-hospital visits, allowing for alternative care provisions that further enhance their quality of life. Telehealth can decrease the number of in-hospital visits and disruptions to everyday life. Objective: This study aims to assess the feasibility of home monitoring with subsequent video consultations and to explore the experiences of families and health care professionals. Methods: We recruited 12 children with cystic fibrosis (>6 y) and their parents from the Cystic Fibrosis-Center Copenhagen. The 5-month intervention consisted of alternately standard care visits and telehealth care visits. Telehealth care visits consisted of home monitoring, with automatically transmitted data to the hospital-based electronic health record. Home monitoring included spirometry, weight, and oxygen saturation, conducted with parental support, along with a video consultation. To monitor lung infections during telehealth care visits, all participants were encouraged to send in a sputum sample beforehand. Families completed preintervention and postintervention questionnaires about time spent, sense of security, and usability of home monitoring devices. We also collected data on families’ and health care professionals’ experiences with telehealth care visits through semistructured interviews after the intervention. Results: All 12 (100%) approached children and their parents accepted to participate. Standard care visits caused full-day absences for 8 (73%) children and 10 (91%) parents, compared to only 1 (8%) child and their parents during telehealth visits. More than 70% of children and their parents rated the oxygen saturation device (n=9, 75%) and weighing scale (n=11, 92%) as “very easy” to use after the intervention. However, 50% (n=6) found the home spirometry device “difficult” or “acceptable” to use, possibly due to device errors. We identified 4 main themes in the interviews: benefits of telehealth, disadvantages of telehealth, prerequisites for telehealth to be successful, and next steps toward telehealth. Conclusions: Home monitoring with automatically transmitted data to the hospital-based electronic health record and video consultations was feasible despite identified concerns regarding technical issues with home spirometry devices.
An Evaluation of the Usability of ReACT (Responsive Asthma Care for Teens), an Adaptive Mobile Health Intervention for Adolescents With Asthma: Feasibility and Acceptability Trial
Background: Adolescent asthma is a significant contributor to youth morbidity and is known to be best managed through consistent medication use and symptom management. However, adolescents often struggle to perceive their symptoms accurately and consistently use their medication at the recommended rate, risking worsened symptoms and impaired quality of life. The Responsive Asthma Care for Teens (ReACT) system is a project aimed at identifying and and providing supporting for several barriers adolescents may face in asthma management. By integrating both software and hardware to monitor medication adherence, ReACT provides a personalized support plan to improve asthma management and, subsequently, quality of life. Objective: The objective of this study is to conduct a proof-of-concept assessment of the ReACT system following an initial pilot study and adjusting for the feedback received. In addition to assessing the acceptability and usability of the current version, this study aims to assess whether the proposed ReACT system leads to indications of improvement in medication adherence because of the personalized support plans. Methods: Participants in the study were 5 adolescents aged 15 to 17 years recruited using a combination of consent-to-contact forms delivered via an in-person asthma clinic and Qualtrics panels. As a part of this study, participants met with the study team 3 times over 1 month. After completing initial surveys on stress, problem-solving, and asthma-related quality of life, we oriented the participants to the ReACT platform and asked them to interact with it as normal. After the month, the participants were interviewed, and they discussed the system and completed surveys assessing their opinions on acceptability and usability. Results: On a 4-point scale, participants reported high acceptability of ReACT (mean score 3, SD 0.32), willingness to use it again (mean score 4, SD 0.89), and willingness to recommend it to a friend (mean score 3.75, SD 0.55), and they considered it to be helpful (mean score 3.2, SD 0.84). Conclusions: Our findings suggest that ReACT is an acceptable and usable mobile health intervention to improve asthma self-management among adolescents, and it had promising results for improving self-regulation, problem-solving, and asthma control. The system continues improving based on feedback from a larger sample size of participants, and we hope that ReACT will aid adolescent development while delivering highly personalized support for each user.
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Text Messaging for Mental Health Promotion With Migrants Returning to Mexico: Content Development and Piloting With a Needs Assessment Approach
Background: Returning migrants face a variety of challenges that limit their ability to integrate and adapt to Mexico. This represents a break in their life trajectory, with effects on family dynamics, mid- and long-term projects, and uncertainty about short-term plans. Objective: This study describes the coproduction approach used to design and develop a WhatsApp-based psychoeducational program entitled “Here Again: Coping With Return,” which aims to promote the adoption of self-care behaviors to reduce the risk of mental health and substance use problems among returning migrants. Methods: The process included four phases: (1) a situational diagnosis of the needs of migrants in preventing mental health problems and reducing the risks associated with alcohol use, (2) the design and development of content, (3) evaluation by a group of experts in mental health and substance use, and (4) pilot testing. Results: The study identified 4 intervention pillars: emotional risk factors, coping strategies, barriers to care, and technological feasibility. Eighty WhatsApp messages were developed, focusing on mental health (n=52, 65%) and alcohol use (n=20, 25%) through a sequence of motivation, instruction, and reinforcement. Following an expert evaluation that simplified technical language, a pilot study with 14 migrants showed a 78.6% completion rate. Participants reported the successful application of emotional management tools and a preference for text-based messages over audiovisual content to conserve mobile data. Conclusions: This study describes the development of a psychoeducational program for returning migrants based on coproduction, integrating user needs and expert experience. The intervention addresses emotional management, self-care, and substance use prevention, using WhatsApp for its accessibility and low cost. The pilot results demonstrated high acceptability and a 78.6% retention rate over 16 weeks, highlighting that the culturally sensitive approach and accessible language enabled participants to apply mental health tools autonomously and effectively.
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When AI Colludes: Clinical Reliability of Training and Preference Data as a Trustworthy-AI Criterion
Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models are themselves clinically reliable, particularly when self-report may be distorted. This Viewpoint aims to develop the clinical psychiatric construct of collusion—the uncritical acceptance of an unreliable account—as an analytic lens for AI training and deployment, and to argue that the clinical reliability of training and preference data should be treated as an explicit trustworthy-AI criterion in mental-health–relevant systems. A conceptual synthesis of psychiatry, clinical psychology, and AI safety literature was undertaken. The analysis distinguishes three pipeline layers: pretraining corpora, preference data and posttraining methods, and deployment-time interaction. It maps the clinical construct of collusion against adjacent technical concepts, including sycophancy, reward overoptimization, grounding, refusal training, red-teaming, and live monitoring. The synthesis suggests that collusion-like dynamics are least applicable at the pretraining layer and most applicable at the preference-data and deployment layers, where unassessed user or labeler input can be reinforced without corroboration. Existing mitigations, including data curation, Constitutional AI, reward-model evaluation, grounded generation, refusal training, red-teaming, and postdeployment monitoring, address parts of this problem. However, these approaches are not yet organized around a clinically informed account of when self-report is unreliable. The central novelty is therefore not a generic claim about bias, but the proposal that clinical self-report reliability should be assessed as a distinct data-quality and governance dimension. Trustworthy-AI frameworks for mental-health–relevant applications should incorporate clinical expertise in self-report reliability into preference-data design, red-teaming, and postmarket surveillance. Adding the clinical reliability of training and preference data as an explicit criterion could complement existing technical safeguards while leaving empirical evaluation of clinician involvement as an open research agenda.
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STAT+: $775 billion, $1.2 billion, and $38k
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Hello, diligent HCI readers! I hope everyone enjoyed their Memorial Day weekends. We’ve got a lot of numbers in today’s edition. Get out your abacus. And tell me if you want more or less math in here: bob.herman@statnews.com.
$775 billion
Centers for Medicare and Medicaid Services
Republicans’ recent tax law targets supplemental Medicaid funds that have increasingly propped up hospitals. The cuts are expected to be even bigger than originally forecast, which almost assuredly will provoke an opposition campaign from hospitals.
GPS Tracking Upends Rural Uganda’s Schistosomiasis Transmission Assumptions
In Uganda’s fishing villages along lakes and rivers, wearable GPS devices are offering a new, detailed picture of how schistosomiasis spreads, helping refine control strategies for a disease affecting about 250 million people globally, mostly in rural sub-Saharan Africa.
A Nature Health study by researchers at the University of Oxford’s Big Data Institute shows that simple models using GPS-tracked movement can accurately predict which open-water sites people use, how often they visit them, and which sites are most likely to drive transmission of Schistosoma mansoni, the parasite that causes schistosomiasis.
“Snail fever”
Schistosomiasis, or “snail fever,” is caused by Schistosoma mansoni flatworms that parasitize freshwater snails. Infection occurs when people contact contaminated water where larval forms of the flatworm penetrate the skin. Repeated exposure leads to reinfection and chronic disease, including liver damage, portal hypertension, bladder fibrosis, kidney damage, and increased cancer risk.
While praziquantel can cure infection, mass drug administration (MDA) with the antiparasitic medication has failed to interrupt transmission of the parasite. The World Health Organization (WHO) notes that MDA alone is insufficient, since transmission persists in localized hotspots. Focal interventions are needed, but researchers have limited them due to poor data on where water contact actually occurs.
Conventional assumptions have limited modeling of human contact with open-water sites. Though open-water contact is heterogeneous within villages and households, it is often assumed that people use only the site closest to their household or village. However, how mobility affects site usage patterns and whether assignment rules beyond nearest distance can be more realistic are unclear.
Along with praziquantel MDA, the 2022 WHO schistosomiasis control guidelines recommend safe water, sanitation, and hygiene (WASH) as the main intervention, but evidence is mixed. Lack of reliable data has made it difficult to determine why the intervention did not improve biannual MDA. Most water contact and WASH studies use self-reported data or household distance to sites and taps, which lacks objective, spatially granular data to characterize fine-scale water usage patterns and quantify WASH’s impact on water contact and (re)infection.
Focal exposure
Lead author Fabian Reitzug, PhD, and colleagues from Goylette F. Chami’s lab tracked 452 people using GPS loggers in three Ugandan districts for 10 days. A total of 8,200 water contact events occurred at 69 open-water sites and 32 improved sources like taps and boreholes—deep drilling to groundwater. Of the participants, 63.9% used open water and 33.2% improved sources.
Reitzug and colleagues found, unsurprisingly, that distance strongly predicted behavior: usage dropped sharply with distance. Open-water contact was ~70% at 20 meters from home and 11% at 500 meters. Nearly all tap and borehole use occurred within 1 km of home, and over 99.5% of open-water contact occurred within 3 km, showing highly localized exposure.
Adding mobility metrics (such as “radius of gyration”) did not improve predictions. This simple finding challenges the assumption that mobile phone tracking can reliably estimate infection risk. Schistosomiasis exposure appears to be caused by local, routine movements, not long-distance travel.
The study found little evidence that safe water infrastructure reduces risky water contact. Taps and boreholes rarely replaced open-water use; fewer than 2% of people fully substituted safe water for natural sources. Daily activities like bathing, fishing, and washing still require lake or river contact. Behavior varied by location. In the Western Ugandan district of Buliisa, nearly 90% visited open water, compared with 44% in the Eastern Ugandan district of Mayuge.
When incorporated into transmission models, GPS-informed movement patterns closely reproduced observed reinfection rates. Simple “nearest-water-source” assumptions overestimated risk. The improved model also identified high-risk water sites by combining human use with ecological suitability for snail habitats.
Targeted interventions at key sites
These findings suggest control programs could shift toward targeted interventions at key transmission sites, such as focal snail control, environmental modification, or localized treatment. The study also indicates that a 1 km intervention radius may be more realistic than the current 500 m guideline. Importantly, reliable spatial patterns emerged from as few as 15 participants over 10 days, suggesting the approach is scalable. Key limitations include using proximity as a proxy for water contact and limited seasonal coverage.
Overall, the study reframes schistosomiasis transmission as a highly local, measurable process, enabling more precise, data-driven control strategies. Future research should examine whether similar models apply to other waterborne diseases. Identifying pathogen-specific exposure pathways and collecting GPS logger data from various locations could test this approach’s generalizability.
The post GPS Tracking Upends Rural Uganda’s Schistosomiasis Transmission Assumptions appeared first on Inside Precision Medicine.
MOSAIC: Multimodal In Vivo Imaging Data Powers AI Models for Living Systems
In a new study published in Nature Methods titled, “A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms,” researchers from University of California, Berkeley present high-powered microscopes that can track the development of live specimens, including cell movement through tissue, the evolution of internal cellular structures, and shuttling of proteins and other molecules within the cell. The system, named Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), has been implemented in more than a dozen worldwide labs over the past six years.
“Life has to be studied in living tissue, holistically, and over fast timescales and for long periods of time,” said Eric Betzig, PhD, professor of molecular and cell biology at UC Berkeley, 2014 Nobel Prize in Chemistry, and co-corresponding author on the study. “You can’t study something as complex as a cell or organism just by looking at the parts individually—there are something like 40 million protein molecules alone of 20,000 different types.”
The microscope uses a large “vision” language model (LVLM), similar to ChatGPT, to measure petabytes of data, the equivalent of about 500 billion pages of text.
Betzig, who is also a Howard Hughes Medical Institute (HHMI) investigator, refers to the imaging data as five-dimensional (5D) composed of three spatial dimensions, plus time and color. The color comes from fluorescent labels that allow scientists to track multiple subcellular structures simultaneously, such as organelles, membranes, the cytoskeleton and more, as they migrate, change shape, divide and interact over time.
In one video, the authors capture a zebrafish regrowing its tail fin. The video revealed tiny events inside living tissue that are normally difficult to visualize, such as cells near the wound releasing small communication packets, microscopic fibers beneath the skin shifting as the tissue repaired itself, two repair cells fusing together and a red blood cell briefly getting trapped as new blood vessels were remodeled.
Ian Swinburne, PhD, assistant professor of molecular and cell biology at UC Berkeley and collaborator on the work, emphasizes that there’s a wealth of information in these large movies across scales, but it can be difficult for a very well-trained biologist to interrogate the data.
“AI can help us interface with the data and ask or answer questions more easily. Like, ‘How many macrophages are crawling into my tissue during an infection?’ or ‘Can I predict when a cell’s going to start leaving its organ?’ That happens in development but also in cancer during metastasis,” said Swinburne.
Building an LVLM or AI that can handle petabytes of imaging data is a main focus of Berkeley’s Advanced Bioimaging Center, which hopes to create a first-of-its-kind Cell Observatory.
“The impact of MOSAIC will be minimal until we build an AI model that can deal with the data that comes out of those systems. We basically have a gold mine, but we have no ability to get the gold out,” said Srigokul “Gokul” Upadhyayula, PhD, assistant professor in residence of cell biology, development and physiology at UC Berkeley. “The primary output of our Cell Observatory Initiative will be an AI mind that’s able to be our scientific partner in extracting these observations.”
The post MOSAIC: Multimodal <i>In Vivo</i> Imaging Data Powers AI Models for Living Systems appeared first on GEN – Genetic Engineering and Biotechnology News.