Fatigue and sleep disturbances are highly prevalent in neurodegenerative diseases (NDDs) and immune-mediated inflammatory diseases (IMIDs). Conventional patient-reported outcomes (PROs) are subjective and prone to recall bias; Digital health technologies and wearable sleep trackers offer objective, continuous monitoring of sleep and physiology at home. This study evaluated the feasibility of using consumer- and research- grade sleep trackers to predict next-day physical and mental fatigue and daytime sleepiness in individuals with NDDs and IMIDs as an exploratory analysis, and examined whether machine-learning models could identify preliminary sleep features to inform future fatigue monitoring research in chronic disease populations. The IDEA-FAST feasibility study enrolled 134 participants (42 healthy adults, 39 NDD, 53 IMID) across four European centres. Over 3,062 nights, participants wore three sleep trackers (BedSensor, ZKONE, DREEM 2) and completed daily fatigue and sleepiness PROs at home. A polysomnography sub-study ( n = 28 ) validated tracker performance. Machine learning models using physiological and sleep-architecture features were evaluated with leave-one-subject-out cross-validation. Sleep trackers showed moderate PSG agreement. Models demonstrated preliminary discriminative capacity for next-day physical fatigue in healthy adults (AUC = 0.75), driven mainly by respiratory rate and REM sleep duration. In NDD, physical fatigue AUC reached 0.62 under enriched training, with REM latency and deep sleep as key features. Mental fatigue prediction reached AUC = 0.66 in healthy adults; daytime sleepiness AUC = 0.66 in NDD. Findings should be interpreted as exploratory, as outcome binarisation using a global threshold may conflate between-person disease-group differences with within-person symptom variation. Wearable sleep trackers show feasibility for objective home-based sleep monitoring, with preliminary evidence supporting sleep physiology as a candidate predictor of next-day physical fatigue in healthy adults. Predictive performance in chronic disease cohorts remains limited, underscoring the need for larger, multimodal studies to establish disease-specific digital fatigue endpoints.
Full-body motion capture using commercial virtual reality (VR) systems offers unique opportunities for augmenting common functional assessment such as the timed up-and-go (TUG) test. The purpose of this study was to determine whether task performance (chair, walk, turns) during a VR version (vTUG) is parametrically equivalent to the standard test (sTUG). Twenty healthy adult participants (age 19-71 years) were evaluated with the sTUG followed by the vTUG version of the same test. Body trackers were used to capture kinematics during both tests. TUG time was measured manually with a stopwatch. Tracker data were used to automatically quantify total TUG time and sub-task times for chair, walk and turn portions. Absolute agreement was evaluated using Intraclass Correlation Coefficient (ICC(2,k)) and Bland-Altman analysis. A custom survey was used to evaluate user satisfaction. Very good agreement (ICC > 0.8) was found between sTUG and vTUG for manual and automated measures of total time. ICCs for sub-task times were acceptable (ICC > 0.7) for chair rise, walks and first turn but less so for second turn and sit (ICC < 0.7). User satisfaction was high, and there were no adverse events. The vTUG and sTUG are parametrically equivalent, though sub-task segmentation may require more research. Nevertheless, VR body trackers are a value-added feature whether used with the vTUG or the sTUG and warrant further investigation.
To validate an activity monitor, the Patient Resident Mobility Tracker (PREEMPT), appropriate for use with hospitalized individuals. Ten healthy older adults and 46 hospitalized patients wore the PREEMPT activity monitor while performing various physical activities. To determine accuracy and validity, the PREEMPT activity data were compared with video truth data. The five mobility metrics included stride length, number of steps, stepping time (total time spent walking), sedentary time and activity time. In addition to the objective data, participants answered 8 Likert scale usability questions about their experience wearing the PREEMPT device. The measurements taken by the PREEMPT device were statistically equivalent to criterion data for all five metrics with a geometric mean ratio ranging from 0.97 to 0.99. Statistical significance values (p) ranged from 0.043 to < 0.001 depending on the metric. The device received an average total score of 9.4 out of 10 on the Likert scale usability questions, indicating that the prototypical device was comfortable to wear and well received. These results demonstrate the initial validity and accuracy for the prototype PREEMPT device in healthy community-dwelling older adults as well as in hospitalized patients. The prototype PREEMPT device accurately measured physical activity in a relatively immobile population residing within a hospital setting. This paper adheres to STROBE guidelines. This research was not a clinical trial and did not require registration.
Space situational awareness increasingly relies on optical observations to detect and track resident space objects and to estimate spacecraft attitude. Many existing resources are synthetic or restricted, and few provide on orbit, wide field of view imagery with joint labels for space objects and stars. We present a dataset of near-infrared images acquired by the Fast Auroral Imager on the CASSIOPE spacecraft between January and August 2023. The collection comprises 1,378 frames with astrometrically calibrated stars and 4,237 manually verified resident space object instances across 160 transits, accompanied by spacecraft ephemeris, attitude, and image quality metrics. We describe the acquisition conditions, calibration and annotation pipeline, and perform technical validation of pointing stability, astrometric accuracy, annotation reliability, and background characteristics. The dataset supports tasks such as resident space object detection in dense star fields, multi-object tracking under realistic orbital motion, and attitude estimation from star tracker class imagery, and is intended as a shared resource for space situational awareness and navigation studies.
Single-molecule enzymes serve as molecular motors for long-read sequencing, where laser tolerance under high photon flux is a critical limiting factor for ultra-long reads. However, elucidating the mechanism of laser-induced enzyme inactivation remains a technical bottleneck due to the lack of long-term, high-throughput single-molecule evaluation methods to decouple intrinsic heterogeneity from photodamage. Here, a digital Single-Molecule Activity Tracker (dSMAT) is presented, combining deep learning with high-throughput digital microfluidics to enable the precision tracking of thousands of compartmentalized single-molecule reactions for 15 h. This strategy reveals a distinct photoinactivation mechanism designated as oxidative scarring through comparative tracking of individual polymerases before and after laser irradiation. This process is driven by the stochastic accumulation of photochemical lesions on redox-sensitive residues (specifically Methionine, Tryptophan, and Cysteine) within functionally accessible pathways, creating a kinetically disordered subpopulation. A synergistic reductive-antioxidant buffer system is engineered to mitigate this effect and rescue kinetic homogeneity. Quantitative cross-platform validation via single-molecule real-time sequencing confirms that dSMAT-derived kinetic metrics-including catalytic rate, heterogeneity, and temporal stability-deterministically govern sequencing read limits. This work establishes a mechanistically sound biophysical framework for the rational design of photostable molecular motors, offering a generalizable strategy for enhancing high-photon-flux enzymology across genomic and biotechnological applications.
Peñaherrera-Carrillo et al. recently presented a systematic review and meta-analysis in the Journal of Robotic Surgery (2026) reporting a pooled periprosthetic fracture incidence of 0.11% following tracker pin placement in 13,217 robotic- and navigation-assisted TKA procedures. Although the article highlights an important and under-reported complication, three methodological concerns directly affecting the validity of these results warrant consideration: the aggregation of biomechanically distinct robotic and computer navigation systems; the absence of bone mineral density data, which is well-documented as the primary host-side risk factor for pin-site fractures; and the omission of platform-specific differences in pin geometry, cortical engagement protocol, and drilling technique. These concerns are intended to contextualize rather than diminish the scientific value of this first comprehensive pooled dataset.
This study aimed to compare sleep architecture, as estimated by a wearable pulse oximeter, between healthcare staff who worked in designated hospitals during the COVID-19 pandemic in Wuhan and a control group, and across a one-year follow-up, and identify factors associated with insomnia risk in this population. Thirty healthcare professionals who worked in Wuhan during the initial COVID-19 outbreak in 2020 and 28 healthy control healthcare professionals who did not participate in epidemic control were recruited. All participants underwent one night of overnight sleep monitoring with a ring-shaped medical pulse oximeter. Psychological health conditions were assessed using the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder (GAD-7), Perceived Stress Scale (PSS-10), Insomnia Severity Index (ISI), and Self-reporting Questionnaire (SRQ-20). A one-year follow-up, including repeat one-night sleep monitoring, was conducted for 28 of the anti-epidemic staff. Twenty-eight anti-epidemic staff and 28 controls completed the study. The difference in total sleep time (TST) among the healthcare staff at the post-deployment assessment, the 1-year follow-up, and the control group was statistically significant (F=9.942, p<0.001). TST of the anti-epidemic group at the post-deployment assessment was significantly longer than that at the 1-year follow-up and that of the control group. A non-significant trend toward a relative decrease in the proportion of deep sleep was observed in the anti-epidemic group after 1 year (F=2.456, p=0.092). In an exploratory analysis, this trend appeared to be driven by a numerical decrease in deep sleep and a numerical increase in light sleep at the follow-up. In a logistic regression model, higher SRQ-20 score and older age were independently associated with increased risk of insomnia, while higher BMI and higher PHQ-9 score showed inverse associations. In this exploratory study, stress exposure may have a sustained impact on the sleep of healthcare staff. SRQ-20 score, age, BMI, and PHQ-9 score were independently associated with insomnia in this cohort. The application of wearable pulse oximeters may serve as a convenient tool for large-scale sleep health screening, but sleep architecture findings derived from these devices require confirmation by polysomnography and should be interpreted with caution.
Optokinetic nystagmus (OKN) is used for objective visual acuity assessment, but precise quantification remains challenging and requires specialised eye trackers. This study uses OKN induced by vernier acuity instead of grating stimuli, recorded with and without eye trackers, to establish an optimised method for acuity measurement and examine its correlation with subjective letter acuity. Thirty-nine adults completed uncorrected monocular testing (letter acuity: -0.10 to 1.00logMAR). Vertically displaced stimuli containing two levels of vernier offsets (0.38 and 9.89 arcmin) moving horizontally were presented. Eye movements were recorded simultaneously using a research-grade eye tracker and a consumer-grade USB camera. Using an automated algorithm, the OKN responses were quantified at each vernier size level, and the reduction between the two levels was registered as a relative OKN index for predicting letter acuity. Subjective letter acuity showed strong correlations with relative OKN indexes from the eye tracker (r = 0.82, p < 0.001) and USB camera (r = 0.76, p < 0.001) recordings, and their letter acuity predictions had mean absolute errors of 0.15 and 0.16 logMAR, respectively. The OKN index demonstrated high accuracy in detecting participants with acuity worse than 0.50 logMAR, achieving receiver operating characteristic (ROC) area under the curve (AUC) values of 0.97 and 0.95 using the eye tracker and camera, respectively. This study provides proof-of-concept for applying OKN-based vernier acuity measurement in adults with or without eye trackers, although extension to a paediatric cohorts requires future investigation.
Glucosylceramide (GlcCer)-based liposomes are glycosphingolipid-rich liposomal systems whose intracellular behavior requires further characterization. In this study, we investigated the intracellular localization and time-dependent behavior of rice bran-derived GlcCer-based liposomes in TIG-103 human dermal fibroblasts. A small amount of BODIPY-labeled sphingolipid was incorporated into the liposomal membrane, and Cascade Blue-labeled dextran was encapsulated in the aqueous lumen to construct a Förster resonance energy transfer (FRET) system. Co-staining with LysoTracker and ER-Tracker was performed to examine intracellular localization. Liposomes encapsulating α-4-methylumbelliferyl glucopyranoside (α-4MUG) or β-4-methylumbelliferyl glucopyranoside (β-4MUG) were also used to assess intracellular substrate processing. After uptake into TIG-103 cells, GlcCer liposomes showed punctate cytoplasmic distribution at early time points together with detectable FRET-related signals. With prolonged incubation, the spatial relationship between the BODIPY signal and the FRET signal changed, and a relatively strong perinuclear BODIPY-positive region became evident. Image-based analyses indicated a higher correspondence of the BODIPY signal with LysoTracker than with ER-tracker/Cascade Blue-related signals. Intracellular 4-methylumbelliferone signals were detected in cells treated with liposomes encapsulating either α-4MUG or β-4MUG. An exploratory investigation of GlcCer liposomes in TIG-103 cells under nutrient-deprived conditions was also examined without fluorescence-based trafficking experiments.
Fitness tracking, facilitated by wearable devices, has emerged as a popular method for monitoring physical activity levels in the general population; however, its usage, acceptance and utility in patients with multiple sclerosis (pwMS) remain understudied. To investigate the prevalence of fitness tracking device usage in pwMS and to explore acceptance and willingness to share data for use in healthcare. We surveyed a cohort of consecutively and prospectively included pwMS diagnosed according to revised McDonald criteria 2017 in MS outpatient departments from two centers in Bern, Switzerland and Vienna, Austria. Out of a total of 200 pwMS (70% female) with a mean age of 43.1 years (SD 12.3 years), 161/200 (80.5%) had relapsing MS. Overall, 34.0% pwMS reported wearing a fitness tracker and when asked how often they actually wear their fitness tracker, 54.4% reported wearing it always and 27.9% reported wearing it often, whereas only 16.2% reported infrequent use. Asked whether they would share the data gathered by their respective fitness tracker, 93.9% reported a willingness to share their health data for both clinical routine care and research. In a representative cohort of pwMS in Central Europe, about one third are already actively and frequently tracking their physical activities with a strong dedication to utilizing their tracking data within healthcare settings including research. This underscores the feasibility and significant potential utility of monitoring physical activity by fitness tracking in the realm of MS care.
UAV tracking is important for aerial surveillance, inspection, and autonomous perception, yet its progress is constrained by the tension between tracking robustness and limited onboard computation. Compared with existing UAV tracking surveys, this review examines UAV tracking from the perspective of architectural evolution under efficiency constraints, and incorporates Mamba- and SSM-based trackers into the analysis. Specifically, this review discusses UAV tracking as a deployment-constrained problem, analyzes CF, Siamese/CNN, Transformer, and Mamba/SSM trackers from a cross-paradigm perspective, and explains how the literature-reported benchmark results should be interpreted under heterogeneous evaluation settings. We then examine how these architectural paradigms, including recent state-space and Mamba-style models, balance representation ability, interaction strength, temporal modeling, and deployment cost under UAV tracking constraints. Finally, we summarize architecture-level trade-offs and outline open problems in preserving local details during sequence modeling, reproducible efficiency evaluation, hardware-aware design, and multimodal UAV tracking.
Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, increases risks of stroke, heart failure, and mortality. Short-term electrocardiographic (ECG) monitoring often misses paroxysmal or asymptomatic AF, underscoring the value of textile ECG platforms for continuous real-world rhythm assessment. To develop and clinically validate a lightweight, interpretable algorithm for AF detection and burden estimation using textile ECG recordings acquired during daily life. We developed Textile AF-Tracker (TAF-Tracker), an RR-interval-based machine learning pipeline using entropy, Lorenz-plot, statistical, and fragmentation features with ECG quality metrics to detect AF in 60-beat segments. Multiple classifiers (Random Forest, XGBoost, Support Vector Machine, logistic regression, and a threshold-based method) were trained on public AF datasets and long-term 3-lead textile ECG recordings (SKIIN™). Data were split by subject into training, validation, and test sets to ensure unseen data were tested. Across public long-term AF datasets, Random Forest and XGBoost achieved 96%-99% accuracy, 95%-99% sensitivity, and 96%-99% specificity. In 14-day textile ECG recordings from 47 AF patients, XGBoost reached 98.4% accuracy (sensitivity 96.1%, specificity 98.7%). AF burden showed a median absolute error of 1.3% (interquartile range 0.7%-1.8%). In healthy and noise stress data, specificity remained ≥99%, even during activity. A lightweight RR-interval-based machine learning algorithm on textile ECG accurately detects AF and quantifies burden in long-term recordings with minimal error. Combined with a comfortable multi-lead textile platform, it provides a practical alternative to Holter monitors and implantable devices for continuous AF surveillance and treatment assessment.
To investigate the regulatory role of epigenetic regulator disruptor of telomeric silencing 1-like (DOT1L) and its mediated histone H3 lysine 79 (H3K79) methylation in modulating neuronal amyloid precursor protein (APP) expression, and to elucidate the underlying mechanisms involving mitochondrial homeostasis and the upstream p38 kinase. Alzheimer's disease (AD) models were established using APP/presenilin-1 (APP/PS1) double-transgenic mice and N2a cells overexpressing the human Swedish mutant APP (N2a-APPswe). Immunofluorescence staining was employed to assess DOT1L expression and localization in mouse brain tissues. N2a-APPswe cells were treated with the DOT1L-specific inhibitor EPZ5676 and divided into four groups: blank control, solvent control, DOT1L inhibitor, and DOT1L inhibitor plus p38 agonist (Gynostemma pentaphyllum extract). Western blotting was performed to measure the phosphorylation levels of DRP1 at Ser616 and Ser637 (key mitochondrial fission regulators), the levels of autophagy-related proteins p62 and the LC3-Ⅱ/LC3-Ⅰ ratio, the phosphorylation level of p38, as well as the expression of APP and APP-processing proteins BACE1 and PS1. Real-time quantitative polymerase chain reaction was used to detect mRNA levels of APP and genes involved in mitochondrial fission and fusion. Proteomics data were systematically analyzed through Gene Ontology analysis, WikiPathways enrichment analysis, and STRING protein-protein interaction network analysis to identify key signaling pathways. Mitochondrial morphology was evaluated by Mito-Tracker fluore-scence staining to measure average branch length. DOT1L expression was signifi-cantly reduced in neurons of APP/PS1 mice compared to wild-type controls. DOT1L inhibition led to decreased H3K79me2 levels (P<0.01), accompanied by a marked increase in APP protein expression (P<0.01), although APP mRNA levels were reduced (P<0.01). Proteomics analysis revealed that differentially expressed proteins were highly enriched in the mitochondrial electron transport chain. Compared with the solvent control, the DOT1L inhibitor group showed inhibited mitochondrial fission, as evidenced by decreased p-DRP1 (Ser616), increased p-DRP1 (Ser637), downregulated MIEF1 mRNA, upregulated MFN1 mRNA (all P<0.05), and increased average mitochondrial branch length (P<0.05), along with reduced p-p38 levels (P<0.05). Co-administration of the p38 agonist significantly reversed these mitochondrial dynamics abnormalities (all P<0.05) and attenuated the abnormally elevated protein levels of APP, BACE1, and PS1 (P<0.05) compared to the DOT1L inhibitor group. DOT1L maintains normal mito-chondrial fission and functional homeostasis through regulation of the p38 signaling pathway, thereby modulating APP expression. 目的: 明确表观遗传调节因子类端粒沉默干扰体1（DOT1L）及其介导的组蛋白H3第79位赖氨酸（H3K79）甲基化修饰对神经元淀粉样前体蛋白（APP）表达的调控作用，并阐明线粒体稳态及其上游p38激酶在该调控过程中的核心机制。方法: 采用APP/早老蛋白1（PS1）双转基因小鼠模型及过表达人源瑞典突变型APP的N2a（N2a-APPswe）细胞模拟AD。通过免疫荧光染色法检测小鼠脑组织中DOT1L的表达和定位。利用DOT1L特异性抑制剂EPZ5676处理N2a-APPswe细胞，分别设置空白对照组、溶剂对照组、DOT1L抑制剂组及DOT1L抑制剂+p38激动剂（绞股蓝提取物）组。采用蛋白质印迹法检测线粒体分裂关键蛋白质DRP1的Ser616和Ser637位点磷酸化水平、自噬相关蛋白质p62水平和LC3-Ⅱ/LC3-Ⅰ比值、信号分子p38磷酸化水平以及APP代谢相关蛋白质APP、BACE1和PS1的表达水平;采用实时定量聚合酶链反应检测APP、线粒体分裂及融合相关基因的表达;基于蛋白质组学数据，通过基因本体分析、WikiPathways富集分析及STRING蛋白质相互作用网络系统筛选关键信号通路;采用Mito-Tracker荧光染色法检测线粒体分支长度。结果: 与野生型小鼠比较，APP/PS1小鼠神经元中DOT1L表达减少。抑制DOT1L后，H3K79二甲基化水平降低（P<0.01），APP表达水平升高但其mRNA表达水平下降（均P<0.01）。差异蛋白高度富集于线粒体电子传递链。与溶剂对照组比较，DOT1L抑制剂组线粒体分裂受到抑制，表现为磷酸化DRP1（Ser616）水平下降、磷酸化DRP1（Ser637）水平升高、MIEF1 mRNA表达水平下降、MFN1 mRNA表达水平升高（均P<0.05），线粒体平均分支长度增加（P<0.05），并伴随磷酸化p38水平下降（P<0.05）。与DOT1L抑制剂组比较，加用p38激动剂可显著逆转上述线粒体动力学异常（均P<0.05），并降低APP、BACE1及PS1的异常高表达（均P<0.05）。结论: DOT1L通过调控p38介导的信号通路维持线粒体正常分裂及功能稳态，进而调控APP表达。.
Glaucoma patients of similar age and educational level, compared with healthy individuals, exhibit a greater number of saccades and fixations during eye-tracking-aloud reading, resulting in reduced reading speed. To evaluate reading performance in patients with glaucoma compared with controls using an eye tracker and to explore the potential confounding effects of patients' contrast sensitivity and cognition. A cross-sectional study was conducted with 111 participants (57 with glaucoma and 54 controls) with a best-corrected visual acuity of ≥0.5 logMAR. Cognition was assessed using the Montreal Cognitive Assessment, and contrast sensitivity was measured with the Freiburg Visual Acuity and Contrast Test. A reading performance evaluation was conducted using the Minnesota Low Vision Reading Test displayed on slides on a computer screen. Reading speed was calculated in words per minute, and an eye tracker was used to analyze saccade and fixation patterns during the reading task. Mean age was 61.8 (±11.6) and 66.5 (±13.7) in the glaucoma and control groups, respectively ( P =0.05). Best-corrected visual acuity was 0.18 (±0.16) and 0.04 (±0.10) logMAR in the glaucoma and control groups, respectively ( P <0.001). Montreal Cognitive Assessment score was 21.8 (±3.5) in the glaucoma and 21.4 (±4.0) in the control group ( P =0.566). A total of 26% of the glaucoma group and 33% of the control group had at least a primary education. Controls read faster and showed fewer saccades and fixations than patients with glaucoma ( P <0.05) across all 5 slides. Patients with glaucoma exhibit poorer reading performance, as evidenced by eye-tracking data, compared with controls of similar age, cognitive function, and educational level.
Clinical decision support systems (CDSS) have emerged as valuable tools for enhancing healthcare for rare diseases. Nonetheless, most tools focus on diagnosis, while few support patient monitoring. We aim to report the methods to develop an evidence-based CDSS for monitoring rare diseases, using Bardet-Biedl Syndrome (BBS) as a case study. We assembled a multidisciplinary team of over 40 healthcare providers from 11 specialities to develop rare disease monitoring plans. We conducted a scoping review to map the existing literature on BBS monitoring, followed by the systematic development of a plan framework with four sections: a tailored medical record with clinical manifestations, a multidisciplinary appointment schedule, a questionnaire tracker, and a complementary exam tracker. We extracted data from articles, books, guidelines, and point-of-care resources. We included 128 references in the analysis. Common study designs included case reports (37.5%), case series (19.5%), and cohort studies (16.4%). We documented 108 clinical manifestations of BBS across ten body systems. The multidisciplinary appointment schedule identified 24 healthcare professionals essential for BBS follow-up, and primary consultations were recommended with 13 specialities. We identified 28 scales and questionnaires, 8 sets of laboratory analyses, 7 electrophysiological studies, and 6 imaging studies for patient follow-up. Our CDSS provides a structured, evidence-based approach to monitoring BBS and improving patient outcomes. This model can be adapted for other rare diseases, promoting comprehensive and multidisciplinary patient care.
Hypertensive disorders of pregnancy (HDPs) are leading causes of maternal and fetal morbidity; yet uptake of home blood pressure monitoring (HBPM) for perinatal detection and management of hypertension remains limited by workflow and integration barriers. We designed and evaluated a clinician-facing HBPM report that integrates blood pressure trends, medication adherence, and pregnancy symptoms to support clinical adoption. Between March 2023 and October 2024, we designed and refined HBPM report prototypes through a mixed method study that included interviews with 16 obstetric providers, using iterative input from a multidisciplinary Clinician Advisory Group. Data on preferences for report features, usability, and workflow integration drawn from provider interviews were compiled and synthesized via convergent quantitative and qualitative methods. Sixteen providers (37.5% nurses, 25% residents, 25% attendings, 12.5% APPs) were interviewed. Providers identified the blood pressure graph, abnormal-value highlights, medication adherence tracker, and symptom log as the most useful features (n = 15-16, ≥90%), while the prenatal vitamin tracker was least useful (n = 9, 56%). Most providers preferred to review the report themselves (n = 11, 73%) and to receive it at a frequency based on clinical need (n = 12, 80%). While most clinicians were willing to integrate the report into their practice, they raised concerns about workflow burden, triage of abnormal readings, and EMR integration as barriers to successful integration. Providers found most elements of the HBPM tool useful for HDP management, but identified workflow and implementation challenges as key barriers, highlighting the need for strategies to support clinical integration.
Digital health interventions are emerging as an approach to support obesity management through self-management and remote care. However, utilization, impact, and practicality remain unclear. This study aims to map research on digital health interventions for obesity management among adults, describing their characteristics, uses, and outcomes, and identifying gaps. A comprehensive scoping review following Arksey and O'Malley's methodological framework, in accordance with the Joanna Briggs Institute's guidelines and reported in line with the PRISMA-ScR guidelines, examined studies published between 2015 and 2025 across four databases: PubMed, Google Scholar, Scopus, and APA PsycNet. A total of 43 studies met the eligibility criteria. Digital health interventions for obesity encompassed consultations and education on healthy lifestyle, behavioral change strategies, physical activity, dietary management, weight goal setting, intermittent fasting, gamification, and psychological support. Digital health interventions were delivered through telehealth, mobile apps, web-based programs, multicomponent digital approaches integrating several digital tools, and hybrid models combining digital delivery with face-to-face communication. These interventions often supported by devices such as digital scales, wearable trackers, and telemonitoring units to enhance self-monitoring, adherence, and engagement. The interventions were implemented across clinical, workplace, and community settings, including adaptations developed during the COVID-19 pandemic. The included studies showed varying and inconsistently reported outcomes, with some showing significant weight loss, improvements in metabolic markers and behavioral outcomes, including dietary adherence, physical activity, and self-monitoring, as well as favorable feasibility outcomes and the potential to maintain continuity of service delivery during the COVID-19 outbreak. Digital health interventions used telehealth, mobile applications, web-based, multicomponent, and hybrid models across different settings, including healthcare settings (e.g., clinical and primary care) and community settings. Digital devices, such as scales, wearables, and activity trackers, were often incorporated to support self-tracking, adherence, and engagement. Reported outcomes included weight loss, improved self-monitoring, and behavioral changes such as enhanced dietary adherence and increased physical activity, and a favorable feasibility outcome; however, these outcomes were not consistently reported across all studies. Key gaps included short follow-up periods and limited evidence from LMICs. Future research should prioritize sustainable, equitable, scalable, culturally adapted, and cost-effective digital interventions.
This study examines differences in response patterns based on associative learning strategies, focusing on goal-tracker (GT) and sign-tracker (ST) profiles in rats. To explore whether maturational processes influence the expression of these phenotypes, Experiment 1 analyzed the distribution of ST and GT profiles across developmental stages (6- and 16-week-old rats). The results indicated a developmental effect on response patterns; younger rats exhibiting a higher prevalence of the GT profile, and no significant sex differences were observed. Experiment 2 assessed the ability of GT and ST animals to suppress their previously expressed behavioral patterns using an omission training procedure. Adult rats demonstrated greater response suppression than adolescent rats. Furthermore, GT profiles showed greater sensitivity to response omission than ST profiles, suggesting that these differences may be related to prefrontal cortex maturation. Sex differences emerged as a crucial factor in adult rats, with female GTs displaying more effective omission than female STs. This finding is particularly important because it may reflect behavioral phenotypes associated with psychiatric disorders, including substance abuse. Taken together, these results highlight how developmental stage and sex influence response profiles in rats, providing translational clinical relevance for understanding core symptoms in neuropsychiatric disorders.
Pedestrian safety is a major concern, especially in heterogeneous traffic conditions like those commonly seen in India. In such complex environments, how the visual attention is directed to different traffic elements, especially under time pressure, plays a key role in understanding their decision-making and improving their situational awareness. To analyze these visual patterns, the present study examines Average Fixation Duration (AFD) as an indicator of visual attention across several Areas of Interest (AOIs), including two-wheelers, cars, heavy vehicles, signal heads, and the intersection area. Experiments were conducted in a virtual environment that simulated a real-world signalised intersection, using a projector-based pedestrian simulator integrated with an eye-tracker. A total of 62 participants completed crossing trials under three experimentally manipulated time pressure levels: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP). The results showed that different levels of time pressure had a clear and significant impact on how pedestrians directed their visual attention toward various Areas of Interest (AOIs). In addition, the study examined other influencing factors, including head-turning behavior before and during crossing, as well as different temporal compliance categories: temporal compliance, non-dangerous temporal non-compliance (TNC), and dangerous TNC. These factors were also found to have a noticeable effect on pedestrians' visual attention patterns. Ultimately, this study provides new insights into pedestrian situational awareness and visual strategies, with practical implications for intersection design.
Oral appliance therapy (OAT) is a widely used treatment for obstructive sleep apnea; however, titration approaches remain variable and lack standardization across clinical practice. Existing evidence is largely derived from academic or specialty sleep centers, with limited data on how titration strategies are implemented and perform in real-world dental settings. This study aims to evaluate real-world titration approaches for OAT within a dental practice-based research network and to assess their impact on treatment effectiveness and patient-centered outcomes. This prospective, multisite, observational feasibility study will enroll approximately 60 adult patients with physician-diagnosed obstructive sleep apnea receiving OAT from 10 dental practitioners within the South Texas Oral Health Network, with each practitioner enrolling up to 6 patients. Practitioners will apply either standard signs-and-symptoms-based titration or enhanced multimethod positioning titration, as per their usual clinical practice. Clinical, dental, and titration characteristics will be collected at baseline and follow-up visits over an approximately 8-week titration period. The primary outcome is mean disease alleviation, calculated as the product of physiologic efficacy (change in apnea-hypopnea index derived from multinight home sleep apnea testing) and objective adherence (hours of nightly oral appliance use captured via an embedded compliance tracker). Secondary outcomes include daytime sleepiness, sleep-related quality of life, patient satisfaction, oral appliance-related side effects, and bruxism. Descriptive analyses and generalized estimating equation models will be used to account for clustering of patients within practitioners. The study was funded on June 17, 2025, and received Institutional Review Board approval from the University of Texas Health Science Center at San Antonio as the single reviewing Institutional Review Board (STUDY00001750), with site-specific approval obtained from the University of Illinois Chicago (SITE00000057). Study start-up activities, including development of data systems and coordination with device vendors, were completed between September and December 2025. Practitioner training was conducted between December 2025 and February 2026. As of March 2026, 7 dentists have been enrolled and have initiated patient recruitment. Participant recruitment began in late 2025 and is projected to continue through January 2027. Data analysis is anticipated in 2027, with study findings expected to be disseminated thereafter. This study will generate preliminary, real-world evidence on oral appliance titration strategies and their relationship to treatment effectiveness and patient outcomes in community dental practices. Findings will inform the design of future interventional trials and support the development of evidence-based guidance for OAT titration.