搜索结果：Movements

Objective assessment of low back pain (LBP) is challenging due to subtle, task-dependent movement impairments that are poorly captured by existing sensing technologies. Motion Tape (MT), which is a self-adhesive elastic fabric skin strain sensor, enables skin-conforming measurement of localized biomechanical strain during functional movement, but its discriminative utility for LBP remains unclear. We examine this question in a multi-sensor, multi-movement setting and analyze whether MT signals encode discriminative structure that distinguishes individuals with LBP from healthy controls. Using data from 20 participants performing 19 functional movements with six sensors, we evaluate movement-specific classification under a leave-pair-out protocol and examine which movements, sensor placements, and features are most informative. Our analysis reveals that group separation is highly selective: only a small subset of movements, most notably forward flexion, consistently supports accurate classification, while many movements remain at near-chance level. We find that temporal dynamics features help in resolving difficult cases that global strain statistics fail to separate, and that informative signals are spatially localized to the lower lumbar spine. In contrast, pretrained time-series foundation models show negligible sensitivity to participant-level structure in MT signals. Overall, the findings from this exploratory study establish when and how MT sensing can effectively differentiate individuals with LBP from healthy controls, providing a principled foundation for larger-scale validation.

Assessment of upper-limb kinematics is essential in clinical practice for diagnosis, rehabilitation monitoring, and treatment personalization. Markerless Motion Capture (MMC) systems, such as the Microsoft Azure Kinect (AK), offer a low-cost and time-efficient alternative to marker-based systems. However, while AK accuracy has been extensively studied for lower-limb movements, its performance for upper-limb analysis-especially under clinically relevant, suboptimal conditions-remains underexplored. This study aims to validate AK for upper-limb motion tracking against a gold-standard optoelectronic system under optimal and suboptimal conditions. Sixteen healthy adults performed ten upper body motor tasks in three scenarios: optimal setup, seated posture with table occlusion, and increased camera distance. Joint angles were compared using normalized Root Mean Squared Error (nRMSE) and Pearson's correlation coefficient. Performance Indicators (PIs) including Range of Motion (ROM), smoothness, and Time to Peak Velocity (TTPV) were also evaluated. AK accurately captured movements performed within the camera plane, with median nRMSE below 20% in optimal conditions and no significant degradation in suboptimal setups. In contrast, movements occurring on planes perpendicular to the camera were poorly captured. ROM estimation was acceptable and highly reproducible, while TTPV showed moderate-to-poor reliability and smoothness deviated substantially from the reference system. These findings suggest that careful attention to Kinect positioning is essential to ensure effective acquisitions, even in suboptimal scenarios. Future research should evaluate AK validity in clinical populations and explore the effects of system interference in multi-device setups.

Facial movements can be key indicators of neurological health and emotional state, offering insights into motor and neuropsychiatric functions that are disrupted in neurologic disorders. Neurological disease can present with characteristic differences in facial movements, like the masked facies of parkinsonism. Automated digital facial expression recognition could assist in asynchronous, remote and objective diagnostic processes. We hypothesized that facial movements relating to smiling, frowning, and blinking could be extracted from brief video-taped encounters in a clinic setting and used to (1) differentiate between neurologic diagnoses, and (2) identify people with symptoms of anxiety and depression. Using untargeted recruitment, individuals with multiple sclerosis (MS), other conditions (parkinsonism, frontotemporal dementia (FTD)), and healthy controls (HC) enrolled in an ongoing digital phenotyping study. Participant faces were video-recorded during a spontaneous language task. Videos were processed using OpenFace 2.2.0, an open-access digital tool pre-trained for facial landmark detection and facial action unit recognition. Participants with MS completed the General Anxiety Disorder-7 (GAD-7) and the Hospital Anxiety and Depression Scale (HADS-D). Videos were analyzed for adults with MS (n=151, mean age 48, 72% female), parkinsonism (n=23, mean age 67, 35% female), FTD (n=14, mean age 68, 29% female), and HCs (n=33, mean age 55, 58% female). Sampling duration was 60-90 seconds; 91% videos passed quality control. Individuals with parkinsonism had decreased eye-blinking compared to all groups, and decreased smiling and increased brow-lowering compared to MS and HCs. Individuals with FTD had increased blinking relative to other groups. There were no significant differences between individuals with MS and HCs. Classification accuracy for partition analysis model was 88% (ROC-AUC 0.84 for parkinsonism). In individuals with MS, decreased variability in brow lowerering was seen with higher anxiety symptoms, and decreased cheek raising intensity was seen with higher depression symptoms. Digitally identified facial movements have face validity for recapitulating known clinical characteristics of neurological disease, as well as reflecting internal state relating to mood. This provides a foundation for expanded longitudinal validation of computer vision-based facial movement analysis in neurological research. However, findings should be interpreted in the context of sample size imbalance across diagnostic groups, which may have influenced classification performance.

Human muscle anatomy consists of multiple layers, each contributing to movement through complex patterns of activation. Conventional non-invasive sensing techniques, such as surface electromyography (sEMG) and mechanomyography (MMG), primarily capture aggregate muscle activity and provide limited depth-dependent information. As different movements may involve distinct combinations of superficial and deeper muscles, access to depth-dependent information could improve the discrimination of motion patterns that are difficult to distinguish using surface measurements alone. To address this limitation, we developed an optical sensor capable of depth-sensitive measurement using near-infrared light. The sensor comprises a light source and an array of photodetectors arranged at six source-detector distances (SDDs) ranging from 12 to 48 mm within a compact wearable module. Two experiments were conducted to evaluate the sensor. First, depth sensitivity was investigated using Monte Carlo simulations and phantom experiments, demonstrating distinct sensitivity profiles for different SDDs and providing preliminary evidence of depth-dependent sensing. Second, the sensor was attached to the forearm to measure signals during nine hand and wrist movements. Machine learning models were evaluated for motion classification, with Linear Discriminant Analysis (LDA) achieving the highest performance. Using all six SDD channels, an average classification accuracy of 87.5% was achieved across 10 subjects. An ablation study evaluating all 63 possible channel combinations further showed that classification performance improved systematically with the inclusion of multiple SDD channels, indicating that measurements obtained at different sensing depths provide complementary information for motion discrimination. These results demonstrate the feasibility of multi-SDD optical sensing for capturing depth-dependent physiological information and highlight its potential as a compact, non-invasive sensing approach for wearable human-machine interface applications.

Background: Drop finger may occur in patients with C7 and/or C8 cervical radiculopathy caused by cervical spondylosis. Although surgical decompression of the affected nerve roots is performed in patients with drop finger refractory to conservative treatment, postoperative recovery of drop finger is often unsatisfactory. Furthermore, no effective rehabilitation strategy for improving drop finger has yet been established. Methods: Here, we report a patient with drop finger who underwent a novel postoperative rehabilitation program. A 64-year-old man presented with drop finger of the left hand caused by left C7 and C8 radiculopathy and underwent cervical foraminotomy. For postoperative rehabilitation, we applied the single-joint Hybrid Assistive Limb (HAL), a wearable robotic suit. The patient underwent a total of 21 sessions of metacarpophalangeal HAL training, which assisted voluntary flexion and extension movements of the metacarpophalangeal joints, and 6 sessions of wrist abduction HAL training, which assisted ulnar-direction wrist abduction movements. Results: As a result, improvement in the left-sided drop finger was achieved. In this case, the use of HAL enabled voluntary motor training within the normal range of motion of the fingers and wrist even during the early postoperative phase, when sufficient neurological recovery had not yet been achieved. Conclusions: This successful motor experience may have facilitated the reacquisition of normal movement patterns, thereby contributing to improvement in drop finger.

Labor pain remains a significant challenge in childbirth due to its impact on maternal comfort and overall birth experience. The Rebozo technique is a traditional non-pharmacological intervention that uses a woven cotton shawl to perform gentle rhythmic movements of the pelvis, aiming to promote relaxation and support labor progression. Despite increasing attention, evidence regarding its effect on labor pain intensity remains fragmented and incomprehensive. This systematic review aimed to synthesize the Rebozo technique in reducing labor pain intensity. A comprehensive search was conducted across PubMed, Scopus, ScienceDirect, and Garuda databases for studies published between 2020 and 2025. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quantitative studies, including randomized controlled trials (RCT) and quasi-experimental designs involving intrapartum women, were included, with pain intensity as the primary outcome. Study quality was appraised using the Joanna Briggs Institute (JBI) Critical Appraisal Tools. A meta-analysis was not conducted due to heterogeneity in study design, intervention protocols, outcome measures, and sample characteristics, and instead a narrative synthesis was applied. Ten studies comprising 537 participants met inclusion criteria. Results indicate that overall Rebozo technique significantly reduced labor pain intensity (p-values 0.000-0.016), particularly during the active phase compared with standard care or other non-pharmacological methods. In conclusion, the Rebozo technique appears to be an effective, safe, and adaptable non-pharmacological approach for labor pain management. Limitations include heterogeneity among studies and restricted generalizability due to the predominance of studies from Indonesia and Türkiye. Further studies with standardized protocols and expanded outcome measure are warranted.

With the rapid advancement of wearable technologies, high-performance flexible sensors have garnered significant research interest. This study presents a PAM-5 hydrogel characterized by exceptional tensile strain (425%), superior compressive modulus (325 kPa), and notable ionic conductivity (1.1 S/m), serving as a robust mechanical framework and electrical foundation for developing advanced sensors. The PNP-5 integrated hydrogel sensor fabricated from this material demonstrates an extensive sensing range (2-53 kPa), remarkable sensitivity, and rapid response time (~321 ms), with its outstanding performance attributed to the synergistic structural design. Furthermore, the sensor exhibits excellent durability, maintaining consistent voltage output (~6.5 mV) across 1000 compression cycles, confirming its long-term operational stability. Through real-time monitoring of physiological signals and biomechanical movements including finger bending, respiration, and grasping, combined with spatial pressure mapping experiments using a 5 × 5 array touchpad, the device's potential applications in wearable sensing platforms and human-machine interface systems are effectively demonstrated. This self-powered hydrogel sensor not only advances the performance metrics of flexible electronic devices but also establishes a solid experimental basis for future development of intelligent materials in health monitoring and interactive technologies.

In the present study, the sublethal neurotoxic effects of monocrotophos (MCP) were investigated using Caenorhabditis elegans, a reliable and widely accepted model organism for studying neurobehavioral changes and toxicological effects. Wild-type N2 worms at the L4 developmental stage were maintained in liquid culture medium and exposed to various MCP concentrations ranging from 75 to 375 μM for 24 h. Locomotion behavioral analysis was done using ANOVA and regression analyses, which revealed a concentration-dependent alterations in several important locomotory parameters. In brief, significant monotonic reductions were observed in track length (44%-92%), peristaltic track length (38%-91%), straight-line distance (88%), peristaltic speed (35%-90%), turn frequency (34%-98%), center point speed (43%-91%), and directional movement distance. Furthermore, non-monotonic responses were observed in behavioral parameters such as bending amplitude, omega bends (99%-100%), and reversal movements (40%), suggesting complex neurobehavioral effects of MCP. The study results clearly showed that MCP exposure severely impacted the locomotor behavior of C. elegans in a dose-dependent manner. This study underscores the high sensitivity of C. elegans to MCP and highlights its potential ecological risks to non-target soil invertebrates. Additionally, these findings emphasize the need to understand the long-term effects of pesticide-induced neurotoxicity in soil ecosystems. Future studies should focus on the molecular mechanisms, biological response pathways, and potential preventive and mitigation strategies associated with MCP-induced toxicity, to develop effective measures to reduce environmental risks and protect soil biodiversity.

Automatic sign language generation has the potential to support information accessibility for deaf and hard-of-hearing individuals. Generating sign language pose sequences from natural language text can serve as an intermediate representation for avatar-based sign language expression and sign language video synthesis. However, text-to-sign pose generation is challenging because sign language conveys meaning through both manual movements and non-manual signals, while requiring temporally coherent motion over local and sentence-level contexts. In addition, text length does not directly correspond to the number of pose frames required for sign language expression. To address these issues, this study proposes a text-to-Korean Sign Language (KSL) pose generation model based on non-manual signal conditioning and multi-scale temporal refinement. The proposed framework integrates a text encoder, pose decoder, non-manual signal conditioning, multi-scale temporal refinement, and length prediction/blending. The model generates normalized 58-joint KSL keypoint sequences from morpheme-level text inputs and jointly optimizes pose reconstruction, motion continuity, bone consistency, PCK-aware precision, non-manual signal prediction, and length consistency. Experimental results on a KSL text-pose dataset show that the proposed model outperforms text-only and Transformer-based baselines. Compared with the Transformer text-to-pose baseline, the proposed model reduced MPJPE from 0.408236 to 0.316366 and Pose MAE from 0.165473 to 0.128570. It also improved PCK@0.05 from 0.136090 to 0.163928 and reduced the length relative error from 0.221455 to 0.127152. In particular, the best-threshold non-manual F1 substantially increased from 0.010859 to 0.494566. These results suggest that text-based KSL pose generation should jointly consider non-manual expressions, length consistency, and long-term temporal motion structure rather than relying only on frame-wise keypoint prediction. However, the reported improvements should be interpreted as coordinate- and label-level evidence, not as a complete validation of linguistic meaningfulness or real-world accessibility.

Inequities in health professions education, including differential attainment, racism, and barriers to widening participation, continue to disproportionately affect students from racially-minoritised and underrepresented backgrounds. While there has been a notable rise in institutional responses to racial inequity since 2020, namely the "Black Lives Matter" movement; institutional responses remain individualized, short-term, or deficit-focused. Responsibility to address inequity is often placed on learners within affected groups, rather than structural conditions that produce and sustain inequity. This Perspective article illustrates the development and evolution of Melanin Medics, a student-founded, community-led organization established in response to inequities experienced by Black medical students and doctors. It aims to critique how community-led approaches can contribute meaningfully to social justice and decolonizing efforts in health professions education. Drawing on organizational practice and reflective insight, the article will position Melanin Medics as a case example of how student-led movements can build social capital, affirm professional identities, and challenge inequitable structures. The article concludes by outlining practical implications and key lessons for health professions educators, institutions, and policymakers seeking to advance meaningful action on equity and inclusion.

Hemophagocytic lymphohistiocytosis (HLH) is a rare but life-threatening syndrome of immune dysregulation with an incidence of 1.2-1.5 cases per million children annually. Curative therapy for primary HLH requires hematopoietic stem cell transplantation (HSCT). A subset of patients develop central nervous system (CNS) involvement, which is associated with high morbidity and mortality yet remains poorly characterized. We describe two patients with genetically confirmed primary HLH (PRF1 mutations with impaired perforin activity) who developed CNS disease. Both presented to the emergency department with fever. Common features included pancytopenia, hepatosplenomegaly, cerebrospinal fluid pleocytosis, and absence of malignancy on bone marrow biopsy. Patient 1: Presented with fever, irritability, abnormal gait, abnormal eye movements, and significant abnormal MRI findings. Patient 2: Following RSV and streptococcal pharyngitis, developed fever, poor oral intake, and lethargy. Both patients received HLH-directed therapy (dexamethasone, etoposide, and emapalumab) plus intensive supportive care. Due to elevated neopterin and CNS disease, both underwent intrathecal chemotherapy to specifically target neuroinflammation. Outcomes diverged: Patient 1 developed refractory status epilepticus and died without transplantation, while Patient 2 successfully underwent HSCT and was discharged home. This case report highlights the heterogeneity of CNS involvement in primary HLH and its impact on outcomes. Elevated neopterin and neurologic manifestations may serve as critical early indicators of CNS disease. Despite advances in management strategies, timely recognition, aggressive treatment, and access to HSCT remain essential to survival. Broader clinical experience and collaborative studies are needed to optimize care strategies for CNS-HLH.

The Brain-Computer Interface (BCI) is a system that enables communication between the brain and external devices by translating brain activity into commands. Electroencephalography (EEG) is a commonly used modality for measuring brain activity. However, its low signal-to-noise ratio (SNR) and electrode reference problems lead to poor spatial resolution. As a result, EEG signals are often contaminated with physiological artifacts such as muscle movements. Therefore, this study used novel tripolar concentric ring electrodes (TCREs) to record brain signals related to overt and covert speech. Brain signals associated with overt and covert speech were recorded using TCRE and disc electrodes. Classification algorithms, including K-Nearest Neighbors (KNN), Fully Connected Neural Networks (FCNN), and Convolutional Neural Networks (CNN), were used to classify the TCRE and conventional EEG signals. The data were collected from 16 healthy participants, consisting of 10 males and 6 females. The experimental results demonstrate that TCREs provide superior performance compared to conventional disc electrodes. In addition, the 0.5-1.2s interval, corresponding to the peak stimulus window, exhibits a maximum power of 250μV. The average accuracy achieved during this peak epoch was 86.25%, whereas the remaining epoch shows an accuracy of 83.5% using TCREs.

The genes coding for the proteins of the ectodermal dysplasin pathway, EDA, EDAR and EDA2R are found in genomes of animals that long predate vertebrates. We show that EDA, EDAR and EDA2R are expressed in the sea anemone. A fourth gene in the pathway, EDARADD, only appears with the cyclostomes. With the appearance of EDARADD, the ectodermal dysplasin system was now complete, and the skin appendages emerged, first in the form of the cyclostome's keratinized proto-teeth, until finally, feathers and then mammalian hair and eccrine glands evolved. We elaborate on the specific roles of the ectodermal dysplasin pathway proteins: EDA releases its extracellular circulating component, switching on the pathway at the appropriate stage in embryological development. EDA binds to EDAR, whose location in the embryo has been specified by a reaction-diffusion system. The EDA/EDAR complex now binds to intracellular EDARADD, directing the expression of downstream proteins that orchestrate the mechanics of the cell movements, building the placodes and the appropriate skin appendages.

To investigate the efficacy of a multidimensional nursing intervention combining Zusanli massage with psychological counselling and breathing exercises in alleviating post-caesarean section abdominal distension, 120 women who underwent caesarean section at Hefei Maternal and Child Health Hospital between September 2025 and March 2026 were selected as the study subjects. Participants were divided into a control group (n=52) and an study group (n=68) based on the nursing intervention protocols they actually received postoperatively. Data were collected and compared regarding the incidence of abdominal distension, indicators of gastrointestinal function recovery, postoperative pain levels, anxiety and depression status, and postoperative complications. The results showed that the incidence of postoperative abdominal distension, pain scores and anxiety and depression scores were all significantly lower in the study group than in the control group. The time taken for bowel sounds to return, flatus passage and bowel movement were all significantly shorter in the study group than in the control group, and the incidence of complications showed a downward trend. This indicates that multidimensional nursing interventions can effectively alleviate postoperative abdominal distension following caesarean section, promote gastrointestinal function and physical and mental recovery, and have positive implications for improving the quality of postpartum maternal rehabilitation. Afin d'évaluer l'efficacité d'une intervention infirmière multidimensionnelle combinant le massage du point d'acupuncture Zusanli, un soutien psychologique et des exercices de respiration pour soulager la distension abdominale après une césarienne, 120 femmes ayant accouché par césarienne à l'hôpital maternel et infantile de Hefei entre septembre 2025 et mars 2026 ont été sélectionnées pour cette étude. Les participantes ont été réparties en un groupe témoin (n = 52) et un groupe d'étude (n = 68) en fonction des protocoles de soins infirmiers effectivement reçus en postopératoire. Les données recueillies et comparées concernaient l'incidence de la distension abdominale, les indicateurs de rétablissement de la fonction gastro-intestinale, les niveaux de douleur postopératoire, les états d'anxiété et de dépression, ainsi que les complications postopératoires. Les résultats ont montré que l'incidence de la distension abdominale postopératoire, les scores de douleur ainsi que les scores d'anxiété et de dépression étaient tous significativement plus faibles dans le groupe d'étude que dans le groupe témoin. Les délais de réapparition des bruits intestinaux, d'émission de gaz et de reprise du transit intestinal étaient tous significativement plus courts dans le groupe d'étude, et l'incidence des complications présentait une tendance à la baisse. Ces résultats indiquent que les interventions infirmières multidimensionnelles permettent de soulager efficacement la distension abdominale après une césarienne, de favoriser le rétablissement de la fonction gastro-intestinale ainsi que la récupération physique et mentale, et contribuent positivement à l'amélioration de la qualité de la réhabilitation maternelle en post-partum.

Growing public concern over the health impacts of particulate matter (PM2.5 and PM10) has highlighted the need for air quality monitoring networks that can raise an alert when high PM2.5 and PM10 concentrations are detected. However, existing networks often have insufficient spatiotemporal resolution to detect localized events with high PM2.5 and PM10 concentrations. In this study, the existing air quality monitoring network in Seoul was complemented by Smart Seoul Data of Things (S-Dot), which is an Internet of Things (IoT)-based sensor network that collects various types of urban data. S-Dot sensors were used to track the spatial distribution and temporal evolution of high-concentration plumes generated by a fireworks festival, and the PM2.5 and PM10 concentrations measured by the S-Dot sensors were corrected with data measured by precision instruments at the closest air quality monitoring station. While the existing high-concentration alert system is designed to issue warnings when averaged PM2.5 concentrations measured at twenty-five air quality monitoring stations across the entire urban area exceed a threshold for a specified duration, the high spatial resolution of S-Dot was leveraged to provide localized alerts in near real time on plume location and movement. A new alert protocol is suggested to help reduce public exposure to pollutants, mitigate associated health risks, and encourage behavioral changes to improve air quality.

Orbital atherectomy (OA) is a highly effective atherectomy device used to treat heavily calcified coronary arteries. The technique for using OA is critical and depends on appropriate use of the dedicated guidewire. OA employs a centrifugal, differential sanding mechanism with bidirectional movement. When used with proper technique, the device appears to be associated with a low rate of complications, such as bradycardia and slow flow, compared with rotational atherectomy (RA), and results in high procedural success rates. We describe our experience with the OA device and procedural techniques in our catheterization laboratory.

Spontaneous movement analysis provides valuable information about the maturation of the central nervous system and the emergence of motor control strategies in very young babies. Nonlinear measures capture dynamic aspects of movement that cannot be represented by linear methods. However, their implementation in clinical practice faces challenges, including the lack of standardized protocols and accessible tools for routine use. This scoping review aimed to map and characterize the nonlinear measures used to analyze spontaneous infant movement, including assessment context, instruments, data collection protocols, and main variables. The review followed JBI methodology and PRISMA-ScR guidelines. Searches were conducted in PubMed®, Web of Science™, IEEE Xplore®, ScienceDirect®, and Google Scholar for studies published from 1 January 2005 to 31 December 2025. Of 1166 records identified, 18 met the inclusion criteria. The nonlinear measures were grouped into five main methodological families: entropy-based measures (n = 10), state-space and dynamical systems measures (n = 4), recurrence-based analysis (n = 3), symbolic and discrete-state approaches (n = 3), and variance and frequency-based nonlinear descriptors (n = 1). Studies were conducted in laboratory settings (n = 6) and in hospital and/or home environments (n = 10). Two studies did not clearly specify the assessment context. Kinematic assessment was mainly performed using video-based systems (n = 7), accelerometers (n = 4), and wearable sensors (n = 2), with most studies focusing on the upper and lower limbs. Several investigations extended beyond single-joint analyses to examine inter-limb relationships and whole-body configurations, capturing spatial coordination patterns across multiple body segments. Kinetic assessment was conducted using pressure mats (n = 4) and force platforms (n = 1), with the center of pressure displacement as the primary outcome. Future research should prioritise methodological harmonisation and theoretical clarity. Consensus is needed regarding minimal data requirements, parameter selection, and reporting standards for commonly used nonlinear measures. Studies should also move beyond single-metric approaches and adopt multivariate frameworks that integrate complementary nonlinear metrics. The absence of standardised acquisition and analytical protocols currently limits cross-study comparability and hinders the clinical translation of nonlinear movement metrics as objective tools for early neurodevelopmental assessment.

Technology is rapidly reshaping paediatric healthcare, offering unprecedented opportunities to improve outcomes, personalise care, and extend reach beyond traditional clinical settings. From AI-driven diagnostics and wearable monitoring to immersive therapeutics and digital mental health tools, innovation is enabling more proactive, child-centred models of care. In the UK, initiatives such as the National Centre for Child Health Technology (NCCHT) and the NIHR HealthTech Research Centre in Paediatrics and Child Health are driving strategic adoption, while international networks including, KidsUp in the US, EPTRI, i4Kids in Spain, ISPI, the WHO's Global Digital Health Strategy and EU-funded paediatric innovation consortia are fostering cross-border collaboration and knowledge exchange. Despite this momentum, significant challenges remain. Fragmented infrastructure, limited interoperability, and uneven digital literacy across the workforce hinder widespread implementation. Regulatory frameworks often lag behind technological advances, particularly in areas like AI, where transparency, bias mitigation, and safeguarding are critical. Moreover, funding pathways for paediatric-specific technologies remain underdeveloped compared to adult-focused innovation. Children and young people (CYP) are increasingly vocal about their expectations for health tech. They value tools that are intuitive, inclusive, and respectful of their autonomy. Feedback from CYP engagement exercises highlights a desire for technologies that support mental wellbeing, facilitate communication with clinicians, and offer personalised insights, without feeling intrusive or overtly clinical. However, concerns persist around data privacy, digital exclusion, and the potential for technology to replace human connection. Ethical considerations are also central to paediatric digital health. AI applications must be transparent, accountable, and co-designed with children and families to ensure they reflect lived experience and avoid unintended harm. Equity must be embedded from the outset, ensuring that innovation does not widen disparities in access, outcomes, or trust. To realise the full potential of technology in paediatrics, we must build inclusive, ethically grounded ecosystems that centre children's voices, support the workforce, and enable safe, scalable innovation. This requires sustained investment, cross-sector collaboration, and a commitment to embedding digital transformation within the broader goals of child health equity and empowerment.

Fulfilling a recognized need in data skills training for academic librarians, the National Library of Medicine and the National Institutes of Health All of Us Research Program collaborated to enhance academic library workers& skills in biomedical and public health data, as well as their library's research capacity, through the awareness and use of the All of Us Researcher Workbench. The All of Us Data Training and Engagement Program for Academic Libraries (ALP) blended professional development training, hands-on learning, and peer-to-peer networking that focused on increasing knowledge of the All of Us Researcher Workbench. Activities were designed to build institution-wide, interdisciplinary awareness and interest in using the All of Us Researcher Workbench. A series of ongoing activities ensured sustained skill-building and collaboration, and included training for R, a program language used for statistical computing and data visualization. Program activities were intentionally designed to help grow institutional research capacity, enhance skills in biomedical and public health data, and promote meaningful use of the Researcher Workbench to campus communities. The ALP helped participants overcome barriers to data access and improve research infrastructure and successfully empowered 115 library professionals to leverage the All of Us Researcher Workbench for meaningful biomedical and public health research. Measured outcomes validate the success of the program and demonstrate how the ALP has positioned participating institutions for long-term success in biomedical and public health research. Institutions can build upon the foundation established through this case report to advance equitable, data-driven health research across academic landscapes.