DIGITAL HEALTH

Background Older adults' walking has so far been evaluated using standardised assessments of walking capacity within a clinical setting. By taking the evaluation out of the laboratory into the real world, this study provides first evidence of the ability of Digital Mobility Outcomes (DMOs) to detect changes over time and the Minimal Important Difference (MID) in patients after proximal femoral fracture (PFF). This will guide the implementation of DMOs in research and clinical care. Methods For this multicenter prospective cohort study, 381 community-dwelling older adults were included within one year after sustaining a PFF and assessed at two time points, separated by six months. Walking activity and gait DMOs were measured using a single wearable device worn on the lower back for up to seven days. A global impression of change question and three mobility-related outcome measures (Late-Life Function and Disability Instrument; Short Physical Performance Battery; 4m gait speed) were used as anchor variables. To assess each DMOs ability to detect changes, we calculated the standardized mean change as effect size. For estimating MIDs, both distribution-based and anchor-based methods were applied, followed by triangulation by experts if at least three anchor-based estimates were available per DMO, resulting in single-point estimates. Results All three anchor variables demonstrated substantial changes. Overall, 10 out of 24 available DMOs showed large and 7 DMOs moderate positive effects in the expected direction of the respective anchors. Seven DMOs showed no or only small effects. For 12 DMOs, at least three anchor-based estimates were available, enabling MID triangulation. MIDs for walking activity DMOs per day were: a walking duration of 10 minutes, a step count of 1,000 steps, 50 walking bouts (WB), and 15 WBs in WBs over 10 seconds. For gait DMOs, depending on the walking bout length, MIDs for walking speed were between 0.04 m/s and 0.08 m/s, and MIDs for cadence between 4 and 6 steps/minute. Almost all DMOs showed a strong ability to detect improvement in mobility, but rarely in detecting decline. Conclusions For the first time, MIDs are presented for real-world DMOs in PFF patients. These MIDs inform sample size requirements and interpretation of intervention effects for clinical trials, thereby providing guidance and reassurance for clinicians and regulatory bodies.

Wearable devices present transformative opportunities for personalized healthcare through continuous monitoring of digital biomarkers; however, individual variations in device wear time could mask or otherwise impact signal identification. Despite the widespread adoption of wearable devices in research, no comprehensive framework exists for understanding how wear time varies across populations or for addressing wear time-related biases in analysis. Using Fitbit data from 11,901 participants in the All of Us Research Program, we conducted the first large-scale systematic assessment of wearable device wear time across demographics, social determinants of health, lifestyle factors, mental health symptoms, and disease. Our findings revealed that wear time was higher among males and increased with age, income, and education, but decreased with depressive, anxiety, and anhedonia symptoms, with reductions more pronounced following clinical diagnoses compared to symptom-based classifications. Individuals with chronic conditions displayed differential levels of wear time compared to healthy controls. Critically, we demonstrate that the widely used [≥]10-hour daily compliance threshold, while appropriate for some research contexts, can disproportionately exclude days of data from disease populations: among individuals with major depressive disorder, 74.4% of data days were excluded compared to 20.9% for controls. We propose a flexible methodological framework including standard compliance thresholds, wear time covariate adjustment, metric normalization, propensity score matching, and adaptive thresholds that can be applied individually or in combination to optimize wearable data retention across diverse research contexts. These findings establish wear time as a critical methodological consideration for wearable device research and provide guidance for advancing equitable and rigorous digital health analytics.

Purpose: Human anatomy remains foundational to clinical practice, yet reduced instructional hours raise concerns about graduate competence and preparedness for patient care. Although trainees often report confidence, supervisors may perceive deficiencies, creating a gap between self-assessment and external evaluation. This study examined stakeholder perspectives on anatomical competence within physical therapy education to identify areas of discordance in perceived capability. Methods: A cross-sectional web-based survey collected responses from 165 stakeholders associated with an entry-level Doctor of Physical Therapy program featuring a 16-week dissection curriculum. Participants rated four domains of anatomical competence using a 5-point ordinal scale. Group differences were analyzed with the Kruskal-Wallis test appropriate for ordinal data. This methodology ensured robust assessment of stakeholder perceptions and comparative analysis. Results: Median ratings of preparedness and capability were 4 of 5 (quite prepared). Significant discordance emerged in three domains: recent graduates rated their foundational knowledge and ability to explain complex concepts to lay audiences higher than faculty or clinical instructors, whereas faculty expressed lower confidence in graduates' ability to explain patient symptoms using anatomical principles. No significant differences were observed in the ability to describe structures by location, suggesting shared perceptions of basic anatomical understanding despite variation in applied reasoning. Conclusions: Stakeholders generally viewed graduates as well prepared, yet disagreement persisted regarding clinical application of anatomical knowledge. Faculty skepticism about symptom explanation indicates that mastery of anatomy alone does not guarantee clinical reasoning. Curricular strategies emphasizing vertical integration and explicit connections between anatomical science and patient-centered reasoning may help bridge perception gaps and enhance professional competence.

Background Persistent inequities in immunisation coverage, particularly among zero-dose and under-immunised children, continue to challenge Pakistan's Expanded Programme on Immunization. Weak feedback loop, inconsistent data quality, and limited real-time monitoring impede effective decision-making. This Implementation Research was conducted under the MAINSTREAM Initiative funded by Alliance for Health Policy and Systems Research (AHPSR) and supported by the Aga Khan Community Health Services Department and National Institutes of Health Pakistan to design, implement, and evaluate a digital monitoring and action planning tool to strengthen data-driven decision-making within routine immunisation systems. Methodology/Principal Findings A co-creation approach was employed to design a digital monitoring solution through inclusive consultations, key informant interviews, and focus group discussions with EPI Punjab at provincial and district levels. The solution included a customised mobile application for data collection and a Power BI visualisation dashboard to map low-coverage areas, identify drivers of dropouts and zero-dose children, and capture caregivers' information sources to inform targeted communication. The intervention was piloted in 60 households across six clusters of a Union Council of District Lahore. Advanced analytics identified reasons for non-vaccination and missed opportunities, generating tailored recommendations and practical plans for program managers. The analysis assessed acceptability, adoption, fidelity, and perceived scalability through field observations, system use, and stakeholder feedback. The co-developed digital tool enhanced visibility of coverage gaps through UC-level mapping, real-time dashboards, and structured action planning. Pilot testing in Lahore showed strong acceptability, ease of use, fidelity, and adaptability among managers, supervisors, and vaccinators. Scalability and sustainability potential were demonstrated, though barriers included leadership turnover, system fragmentation, workload pressures, and resource constraints. Conclusion The tool demonstrated feasibility to strengthen immunisation equity, accountability, and responsiveness. Co-creation with stakeholders enhanced ownership, operational relevance, and adoption, while complementing existing platforms. Sustainability will depend on effective integration, local ownership, capacity building, and accountability, while scalability requires interoperability, resource commitment, policy support, and alignment with existing workflows.

Thyroid carcinoma is one of the most prevalent endocrine malignancies worldwide, and accurate preoperative differentiation between benign and malignant thyroid nodules remains clinically challenging. Diagnostic methods that medical practitioners use at present depend on their personal judgment to evaluate both imaging results and separate clinical tests, which creates inconsistency that leads to incorrect medical evaluations. The combination of radiological imaging with clinical information systems enables healthcare providers to enhance their capacity to make reliable predictions about patient outcomes while improving their decision-making abilities. The study introduces a deep learning framework that utilizes multiple data sources by combining magnetic resonance imaging (MRI) data with clinical text to predict thyroid cancer. The system uses a Vision Transformer (ViT) to obtain advanced MRI scan features, while a domain-adapted language model processes clinical documents that contain patient medical history and symptoms and laboratory results. The cross-modal attention system enables the system to merge imaging data with textual information from different sources, which helps to identify how the two types of data are interconnected. The system uses a classification layer to classify the fused features, which allows it to determine the probability of cancerous tumors. The experimental results show that the proposed multimodal system achieves better results than the unimodal base systems because it has higher accuracy, sensitivity, specificity, and AUC values, which help medical personnel to make better preoperative decisions.

Background: workforce innovative solutions are warranted to respond to the critical global lack of healthcare professionals and sustain delivery of quality patient care. The Patient Care Technician program was one of the strategies implemented to address this challenge by developing a timely pool of workforce who can take non-complex tasks, alleviating workload on other professionals such as registered nurses. However, since this strategy was recently introduced, its implementation and impact on the delivery of care have not yet been sufficiently investigated. Objectives: This study examines the motivations, experiences, and career aspirations of patient care technician students, alongside program providers perceptions and challenges in program delivery. Design & Methods: A qualitative phenomenological study was conducted at three institutions in Western Saudi Arabia, including two tertiary hospitals and a university. Semi-structured interviews were conducted with 27 participants; students, lecturers, preceptors, and management staff. Policy documents were also analyzed, and data were examined using Colaizzis seven-step method. Findings: Four key themes emerged: (1) reconciling motivations and influences, (2) training dynamics, (3) career advancement, and (4) navigating acceptance. patient care technician students often felt overqualified for their roles, leading to dissatisfaction and career redirection. The programs effectiveness was hindered by unclear career pathways and the need for greater cultural sensitivity. Conclusions: Recruiting bachelors degree graduates for patient care technician students roles may be inefficient, as these positions could be filled by lower-degree holders, potentially reducing costs. Implications: To enhance workforce stability, healthcare policymakers should establish clear career pathways, align job roles with educational qualifications, and adapt the program to local cultural and professional expectations. Addressing these issues can optimize the roles of patient care technician students within the healthcare system and serve as a model for similar workforce strategies globally.

Background: In low- and middle-income countries, the burden of hypertension is increasing. Medication adherence is a critical component of reducing hypertension-related cardiovascular disease (CVD) risk and death. There are many barriers to hypertension medication adherence, including challenges with access to and possession of medication. To address these challenges, we aim to implement a strategy in rural western Kenya that combines peer delivery of medications and health information technology to improve hypertension medication possession and adherence. Recognizing that stakeholder experience and knowledge can be useful to optimize successful implementation, we sought to assess micro- and macro-level stakeholder perceptions of the planned implementation strategy. Methods: Focus group discussions in both English and Kiswahili were conducted among people living with hypertension, community members, and health workers. In addition, key informant interviews were conducted with public sector health administrators including the program/policy planners for non-communicable diseases at the national and county levels. Content analysis of all transcripts was conducted. A codebook containing deductive codes was generated based on a priori themes identified from the interview guide. These included the perceptions of peers being involved in health service provision, medication delivery, psychosocial support, and the use of health information technology. Emerging themes were also identified and integrated into the results. The investigator team pooled codes according to conceptual alignment and integrated them into common themes after joint review and discussion. NVIVO 12 was used for the data analysis. Results:The PT4A implementation strategy was perceived to have both benefits and potential challenges. Major themes included the importance of trust resulting from a safe space to share experiences with peers, increased access to medications, improved hypertension management at the facility and community levels, and anticipated improved health outcomes for people living with hypertension. The success of the program was felt to rely heavily on the peers competency and how well they communicated, which was viewed as a potential challenge by some stakeholders. Areas of consensus expressed across all participant groups were mostly focused on patient psychosocial support and access to medications. Conclusion: This study was able to identify key perceptions elicited for an implementation strategy that combines peer medication delivery and health information technology to improve hypertension medication adherence. Pre-implementation stakeholder engagement can unearth unique perspectives around perceived benefits and challenges that can be used to refine strategies to increase the success of implementing evidence-based interventions in new contexts.

Background: Nursing interns are at high risk of psychological distress due to academic and clinical stressors. While poor sleep quality is linked to anxiety and depression, the buffering role of social support remains underexplored in this population. Aims: To explore the role of social support in regulating the relationship between sleep and mental health among nursing interns. Methods: A total of 396 nursing interns completed self-administered questionnaires including the Pittsburgh Sleep Quality Index (PSQI), Social Support Rate Scale (SSRS), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). Hierarchical regression and simple slope analyses were used to test moderation effects. Results: Poor sleep quality was significantly associated with higher anxiety ({beta}=0.449, P<0.001) and depression ({beta}=0.535, P<0.001). Social support significantly moderated these relationships. Under low social support, the effects of sleep quality on anxiety ({beta} = 0.602) and depression ({beta} = 0.779) were stronger than under high support (anxiety: {beta} = 0.396; depression: {beta} = 0.515). Conclusions: Social support buffers the adverse psychological effects of poor sleep among nursing interns. Interventions should integrate sleep hygiene education with strategies to enhance social support.

Background: Type 2 diabetes is a growing challenge in low- and middle-income countries, where health systems face major capacity gaps. Participatory learning and action (PLA) has shown effectiveness in preventing type 2 diabetes in Bangladesh, but little is known about its use in other LMICs for diabetes. The EMPOWER-D (Engagement of community through Participatory learning and action for cOntrol and prevention of type 2 diabetes) trial is testing PLA for diabetes prevention in communities in Pakistan and Afghanistan. This protocol describes the plans for the embedded process evaluation (PE). Methods: The PE will use a mixed-methods design across three sites, following the UK Medical Research Council framework for PE, examining implementation, mechanisms of impact and context. Implementation will be assessed using adaptation reports, fidelity checklists, attendance data, and supervisor reports. Mechanisms of impact will be explored through interviews, focus group discussions and photovoice. Contextual factors will be examined through interviews with participants, community mobilisers, supervisors, and key stakeholders. Quantitative data will be analysed descriptively, while qualitative data will undergo thematic analysis using a theory of change framework. Comparative analysis will identify common and context-specific influences. Discussion: This is the first multi-country PE of a PLA intervention for diabetes prevention to our knowledge, and the first in Afghanistan and Pakistan. The study will provide insights into how the intervention was delivered, how and why it worked (or did not work), and the contextual factors shaping outcomes. Findings will inform the adaptation and scale-up of participatory approaches for non-communicable disease prevention in resource strained setting health systems.

Objective To test the effectiveness of a postpartum behavioural intervention delivered by automated text messaging in reducing weight. Design Two parallel group, multicentre, randomised controlled trial. Setting Recruitment from five areas across the United Kingdom (Belfast, Bradford, Stirling, London and Cardiff) through healthcare and community pathways, including social media. Participants A diverse sample of 892 women between 6 weeks and 24 months postpartum, aged 18 years or more and with a body mass index of 25 kg/m2 or more, enrolled between May 2022 and May 2023: 445 were randomised to the intervention and 447 to an active control (comparator). Interventions Twelve months of fully automated text messages with embedded behaviour change techniques and two-way messaging components to support weight loss and maintenance of weight loss in the postpartum period by targeting dietary, physical activity and weight management behaviours. The comparator group received 12 months of text messages on child health and development tailored to child age. Main outcome measures Primary outcome: weight in kilograms at 12 months (end of intervention). Secondary outcomes recorded at 6 and 12 months were changes in weight (at 6 months), body mass index, proportions of women with weight gain or loss of 5 kg or more, waist circumference, self-reported dietary intake, physical activity and infant feeding practices. Results 674 (75.6%) participants were included in the primary analysis. There was no statistically significant difference found in the adjusted mean weight change between the intervention and active control groups (-0.1 kg (95% confidence interval -1.0 to 0.8, P= 0.84). Sensitivity analyses did not change these results. There was a small statistically significant improvement in Fat and Fibre Barometer scores at 12 months in the intervention compared with control group (adjusted mean difference 0.09, 95% CI: 0.04 to 0.14; P <0.001) and a statistically significant increase in physical activity scores (International Physical Activity Questionnaire Short Form) at 12 months in the intervention group compared with the control group (adjusted mean difference 405.3 total MET minutes/week, 95% CI: 141.3 to 669.3; P= 0.003). Conclusions A 12 month automated, interactive behavioural weight management intervention delivered by text message did not support weight loss for postpartum women but did have a positive impact on diet and physical activity behaviours.

Abstract Introduction Cardiovascular disease (CVD) is an important complication of type 2 diabetes (T2D). Current incident CVD-prediction models use single baseline measurements and achieve moderate performance in people with T2D, with C-indices around 0.7. Modern healthcare registries contain repeated measurements of HbA1c, LDL-cholesterol and eGFR, which could carry incremental predictive value. However, the added value of trajectory measures for CVD-risk prediction remains unclear. We aimed to investigate the utility of HbA1c, LDL-cholesterol and eGFR trajectory measures for incident CVD-risk prediction in people with T2D. Methods We studied 83,326 people with T2D from Danish nation-wide registers, who were without a CVD-history at baseline (January 1st 2015), and had [&ge;]2 recorded HbA1c, LDL-cholesterol and eGFR measurements between 2012-2014. Their last measurement was considered as baseline. Across 2012-2014, three types of paired trajectory measures were calculated for each participant (mean & standard deviation (SD), median & interquartile range (IQR), and intercept & slope from a fitted growth model), for HbA1c, LDL-cholesterol, and eGFR, respectively. Reference Cox-regression models for CVD-events (ICD-10 codes assessed prospectively from 2015- 2020) included only baseline measurements (age, sex , age at T2D onset, HbA1c, LDL-cholesterol, HDL-cholesterol, eGFR, and medication use). Next, the paired trajectory measures were sequentially added to the reference model, computing Hazard Ratios, C-indices and Net reclassification index (NRI) with 95% confidence intervals. Lastly, a combined model was fitted. Results At baseline, mean age was 65 (SD{+/-}12), median HbA1c was 48 (mmol/mol, IQR43-56), and 48% were female. During a median 6 years of follow-up 11,280 (14%) people had a CVD-event (ischemic heart disease: 40%; stroke: 32%; heart failure: 24%; CVD-mortality: 5%). Accounting for the reference model, trajectory measures of dispersion and change were associated with CVD-events, with hazard ratios {approx} 1.1 for HbA1c and eGFR, and >1.4 for LDL-cholesterol. Measures centrality did not show an association with CVD events. Addition of trajectory measures produced minimal gains in discrimination (C index {Delta} +0.001-+0.003) but modest improvements in net reclassification (continuous NRI {approx} +3-+9%). Conclusions Trajectory dispersion or change measures for HbA1c, eGFR and especially LDL-cholesterol, easily obtained from routine data, might moderately enhance incident CVD-risk prediction in people with T2D.

Deficits in inhibitory control are common across a wide range of psychiatric disorders and are closely linked to symptom severity, including emotional dysregulation, anxiety, substance misuse, and self-harm, making them an appealing target for intervention. Cognitive training offers a low-cost, scalable, and non-invasive strategy to strengthen inhibitory control; however, most existing paradigms target only a single facet of inhibition and rarely account for environmental influences, such as affective context. To address these gaps, we developed a computerized inhibitory control training paradigm to simultaneously engage three components of inhibition: preemptive, proactive, and reactive, while embedding trials within positive and negative affective contexts to assess the impact of emotional stimuli. Across two online experiments, participants completed the GAMBIT task in one session (Experiment 1, N = 300) or repeated over three sessions (Experiment 2, N = 65). The task included No-Go trials to train preemptive inhibition, stop-signal trials for reactive inhibition, and stop-signal anticipation trials to train proactive inhibition. Affective images of differing valence were presented as background stimuli to evaluate their impact on inhibitory performance. In Experiment 1, participants showed higher accuracy on No-Go versus reference Go trials ({beta}=1.45, SE=0.09, p<.001), confirming successful manipulation of preemptive inhibition. Reaction times were slower during anticipation trials across two different conditions ({beta}=0.16, SE=0.04, p<.001; {beta} = 0.07, SE = 0.04, p = 0.047), consistent with proactive slowing when anticipating a potential stop signal. Additionally, positive affective images ({beta} = 0.10, SE= 0.009, p < 0.001) further slowed RTs, indicating emotional interference with proactive control. In Experiment 2, the pattern of higher No-Go accuracy was replicated ({beta} = 0.91, SE = 0.11, p < .001) and accuracy generally improved over sessions ({beta} = 0.38, SE = 0.06, p < .001). In anticipation trials, RTs become shorter across sessions (session 2: {beta} = -0.25, SE = 0.06, p < .001; session 3: {beta} = -0.45, SE = 0.06, p < .001), reflecting practice-related gains, and SSRTs decreased over time (F(2,56) = 6.26, p = .004), consistent with enhanced reactive inhibition. Proactive inhibition was modulated by affective images, with both negative ({beta} = 0.04, SE = 0.02, p = .039) and positive ({beta} = 0.16, SE = 0.02, p < .001) affective images associated with slower RTs. Participants also reported reductions in self-assessed temper control by the last session (W = 25.5, p = .007, q = .037, d = -0.51) and usability ratings were high (all means [&ge;] 3.87/5). Together, these findings show that this paradigm recruits multiple forms of inhibitory control and yields training-related improvements in both performance and affective outcomes. This provides preliminary validation of a scalable, fully online inhibitory control training tool targeting multiple dissociable inhibitory processes within affective contexts. The approach holds promise as an accessible transdiagnostic intervention to support symptom improvement across psychiatric disorders, with future work needed to evaluate clinical efficacy in patient populations.

Gait variability is a critical functional indicator of dynamic balance and neurocognitive decline in health. Its translation into clinical practice is, however, challenged by a lack of age-related normative trajectories and reference values under real-world ecological settings. Furthermore, the conventional metrics used to estimate gait variability (Coefficient of Variation, CV; Standard Deviation, SD) have a fundamental methodological flaw: the inherent sensitivity of conventional metrics to the statistical outliers and environmental noise in real-world walking. In this study, we mitigate this factor by applying a robust statistical framework to quantify gait variability. Analysing a large-scale cohort of community-dwelling older adults (n=2,193), we first demonstrate that free-living gait data follows a heavy-tailed distribution, necessitating the use of robust estimators like the Robust Coefficient of Variation (RCV-MAD) and Median Absolute Deviation (MAD). Leveraging these metrics, we established the normative trajectory and reference values of real-world gait variability across the ageing lifespan, revealing a distinct, age-dependent increase in spatio-temporal fluctuations, indicating a decline in rhythmicity and steadiness with age. We further demonstrated the clinical utility of these robust metrics: RCV-MAD consistently yielded larger effect sizes than conventional CV in discriminating between fallers and non-fallers across all gait parameters. Furthermore, we illustrate the potential of long-term unsupervised monitoring to capture intrinsic variability during real-world walking. Validated for consistency and reliability, this robust framework provides the necessary ecological validity to transform gait variability into a standardised, rapid clinical metric for assessing functional decline at an early timepoint.

Actigraphy is a popular behavioral sleep assessment tool in research and clinical practice. Hierarchical hand-scoring approaches remain the standard for actigraphy rest interval estimation, but can be impractical for large cohort studies and suffer from reproducibility problems. We developed a semi-automated pipeline (actiSleep) to set rest intervals consistent with best-practice hand-scoring algorithms incorporating event marker, diary, light, and activity data. To evaluate actiSleep performance, we used data from an observational study of 51 adolescents (14-19yr), with and without family history of bipolar disorder. Participants completed 2 weeks of wrist actigraphy and daily sleep diary. We first hand-scored records using a standardized hierarchical algorithm incorporating event marker, diary, light, and activity data. We then compared the hand-scored rest intervals to those from actiSleep and two automated activity-based algorithms (Activity-Merged, Activity-Only). Activity-Only used activity-based sleep estimation and Activity-Merged joined closely adjacent rest intervals. For rest onset, rest offset, and rest duration, all algorithms had strong mean agreement with hand-scoring: actiSleep estimates were within 1-3 minutes, Activity-Merged within 2-4 minutes, and Activity-Only within 7-14 minutes. However, actiSleep had notably better (narrower) margins of agreement with hand-scoring, as evidenced by Bland-Altman plots, and greater positive predictive value and true positive rates for rest detection, especially in the 60 minutes surrounding the onset and offset of the rest interval. The actiSleep algorithm successfully estimates actigraphy rest intervals comparable to hand-scoring while avoiding pitfalls of activity-only algorithms. actiSleep has potential to replace hand-scoring for research in adolescents but requires further testing and validation in other samples.

Background and objectives STXBP1-related disorder (STXBP1-RD), caused by pathogenic variants in the STXBP1 gene, is a rare neurodevelopmental condition, characterized by early-onset seizures, developmental delay, intellectual disability (ID), and prominent motor dysfunction. Despite the high prevalence of motor symptoms, systematic gait characterization remains limited. We therefore aimed to quantitively assess gait in individuals with STXBP1-RD. Methods In this cross-sectional study, we included ambulatory patients aged 6 years or older with genetically confirmed STXBP1-RD. Instrumented 3D Gait Analysis (i3DGA) was performed to objectively quantify gait. Functional mobility was assessed with the Functional mobility scale (FMS) and Mobility Questionnaire 28 (MobQues28). Caregiver health-related quality of life was evaluated using the PedsQL-Family Impact Module (PedsQL-FIM). We explored associations between gait, functional mobility, STXBP1-variant type and clinical features (ID, age at seizure onset, seizure frequency, age at onset of independent walking). Correspondence between i3DGA and the Edinburgh Visual Gait Score (EVGS), an observational gait assessment, was investigated. Results Eighteen participants were included. Compared to typically developing peers, individuals with STXBP1-RD had significantly reduced walking speed, step and stride length. Gait patterns were highly variable, with the most frequent pattern being an externally rotated foot progression angle (FPA), present in 11/18 participants. At home, 93.75% of the participants (16/18) walked independently, yet community mobility was more variable: 11/16 (68.75%) walked independently, 2/16 (12.50%) with aid and 3/16 (18.75%) used a wheelchair, indicating increasing limitations with distance and environmental complexity. Earlier acquisition of independent walking strongly predicted later unassisted ambulation at community level (p<0.001). Median MobQues28 score was 57.14% and median PedsQL-FIM score was 60.42%, indicating a moderate level of mobility limitations and reduced health-related quality of life of caregivers. EVGS was highly positive correlated with i3DGA (p= 0.001). Discussion Quantitative gait analysis in individuals with STXBP1-RD demonstrates heterogenous kinematic deviations, with an externally rotated FPA emerging as the most common pattern. Age at independent walking was a clinically relevant predictor of later functional mobility. EVGS showed strong correspondence with i3DGA and may offer a more practical, semi-quantitative assessment for broader use. These findings inform clinical decision-making and guide the selection of scalable outcome measures for natural history studies and interventional trials.

Background and Purpose: Magnetic resonance imaging (MRI) for radiation therapy treatment planning is currently being used in many anatomical sites to better visualize soft tissue landmarks, a technique known as an MRI simulation. A core component of modern MRI simulation configurations are the use of external laser positioning systems (ELPS) to help set up the patient. Though necessary for accurate and reproducible patient setup, the ELPS, if left on during imaging, may interfere negatively with image quality due to leaking electronic noise, of which MRI is sensitive to. It is currently unknown whether this leakage of electronic noise may further affect quantitative values derived from clinically employed relaxometric, diffusion, and fat fraction sequences. Therefore, in this study, we aim to characterize the impact of MRI simulation lasers on general image quality and quantitative imaging accuracy. Materials and Methods: First, a cine acquisition was used to visualize the real-time changes in image signal-to-noise ratio (SNR) from when the ELPS was deactivated to activated. To validate this effect quantitatively, the SNR was measured using the American College of Radiology (ACR) recommended protocol in a homogeneous phantom with the integrated body, 18-channel UltraFlex small, 18-channel UltraFlex large, 32-channel spine, and 16-channel shoulder coils. Next, a geometric distortion algorithm was tested in two vendor-provided phantoms while using the integrated body coil and the ACR Large Phantom protocol was tested. Finally, a series of quantitative MRI scans were performed using a CaliberMRI Model 137 Mini Hybrid phantom to validate quantitative T1, T2, and ADC while a Calimetrix PDFF-R2* phantom was used for quantitative PDFF and R2*. All scans were performed with both the ELPS both deactivated and activated. Results: Visible electronic noise artifacts were seen when using the integrated body coil when the ELPS was activated on the cine acquisition which led to a four-fold decrease in SNR using the ACR protocol. This SNR drop was not seen when using the remaining tested coils. The automatic fiducial detection algorithm was affected negatively by ELPS activation leading to misidentification when identified perfectly with the ELPS deactivated. Degradation in image intensity uniformity, percent signal ghosting, and low contrast object detectability was seen during ACR Large Phantom testing using the 20-channel Head/Neck coil. Concordance across quantitative MRI values was similar when the ELPS was both deactivated and activated while a consistent increase in standard deviation inside the ADC vials was seen when the ELPS was activated. Discussion: The extra noise induced from the activation of the ELPS during imaging should be avoided due to its potential to unnecessarily increase image noise. This is particularly true when conducting mandatory quality assurance testing for image quality and geometric distortion which utilize the integrated body coil which is most susceptible to ELPS-induced noise. Clear clinical guidelines should be implemented to make this issue known to the MRI technologists, physicists, and other relevant staff using an MRI with a supplementary ELPS for patient alignment.

Understanding host susceptibility to Mycobacterium tuberculosis (Mtb) is critical for the development of new vaccines. Certain individuals "resist" becoming infected with Mtb despite intensive exposure; however, it is unknown whether there is a genetic basis for "resistance" to Mtb infection across populations. Here we conducted a genome-wide association study (GWAS) of resistance to Mtb infection by carefully characterizing exposure to TB patients among 4,058 close contacts in India, Brazil, and South Africa. 476 (12%) "resisters" remained free of Mtb infection despite substantial exposure to highly infectious TB patients. GWAS identified a novel chromosome 13 locus (rs1295104126) associated with resistance across the multi-ancestry meta-analysis. Comparing Mtb-infection to all uninfected contacts, irrespective of exposure, yielded a different locus on chromosome 6 (rs28752534), near the HLA-II region. These findings demonstrate a common genetic basis for resistance to Mtb infection across multi-ancestral cohorts with potential to elucidate novel mechanisms of protection from Mtb infection.

Background Artificial intelligence (AI) is increasingly being integrated into healthcare systems, with growing applications in clinical decision support, workflow optimization, and population health management. While substantial investments have been made in digital infrastructure, the successful adoption of AI in primary care depends critically on the readiness, awareness, and educational preparedness of healthcare professionals. Global health authorities emphasize the need for ethically grounded and workforce-focused approaches to AI integration; however, evidence on clinicians readiness for AI, particularly in primary care settings and in the Middle East region, remains limited. Objectives This study aims to assess the level of awareness, perceptions, attitudes, and educational needs related to AI among healthcare professionals working within Qatars Primary Health Care Corporation (PHCC). In addition, it seeks to examine organizational factors influencing the integration of AI-focused education in primary care and to develop an AI readiness framework that can inform targeted training strategies and policy planning. Methods This study will adopt a mixed-methods design guided by the Organizational Readiness for Change (ORC) framework, adapted for AI integration in primary care. The quantitative component will consist of an anonymous, census-style online survey distributed to all healthcare professionals across PHCC health centers and headquarters, assessing AI awareness, attitudes, training needs, and perceived infrastructure readiness. Composite AI awareness and attitude scores will be calculated, and regression analyses will be used to explore factors associated with AI readiness. The qualitative component will include semi-structured interviews and focus group discussions using maximum variation sampling to capture diverse professional perspectives. Qualitative data will be analyzed thematically, following COREQ and SRQR reporting standards. Quantitative and qualitative findings will be integrated to generate an AI readiness profile and an actionable education roadmap aligned with national digital health priorities. Discussion This study will provide the first comprehensive assessment of AI readiness among primary care healthcare professionals in Qatar. By identifying knowledge gaps, training priorities, and organizational enablers and barriers, the findings are expected to inform the development of evidence-based AI education strategies within continuing professional development frameworks. The proposed AI readiness framework may also offer a transferable model for other health systems seeking to align workforce development with responsible AI implementation in primary care.

Background: Quadriceps weakness may reduce sagittal plane shock absorption during landing, shifting load toward the frontal plane and increasing knee abduction moment (KAM), a biomechanical risk factor for anterior cruciate ligament (ACL) injuries. Purpose: The purpose of this study was to evaluate the association between isokinetic quadriceps strength and peak KAM during drop vertical jump landing in adolescent athletes. Study Design: Secondary analysis of previously collected data. Methods: Healthy adolescent athletes completed quadriceps strength testing using an isokinetic dynamometer and a biomechanical assessment during a drop vertical jump task. Quadriceps strength was quantified as peak concentric torque and the peak external KAM was calculated during the landing phase on the dominant limb. Both strength and KAM were normalized to body mass. Linear regression was used to examine the association between normalized quadriceps strength and peak external KAM on the dominant limb. Results: The association between quadriceps strength and peak normalized KAM on the dominant limb was not statistically significant ({beta} = -0.053 (95% CI [-0.137 to 0.030]), F(1,119) = 1.62, R2 = 0.013, p = 0.206). Quadriceps strength explained only 1.3% of the variance in peak KAM, indicating a negligible association between these variables in this cohort. Discussion: Quadriceps strength was not associated with peak normalized KAM during landing, suggesting that frontal-plane knee loading during a drop vertical jump is not meaningfully explained by maximal concentric quadriceps strength alone. KAM appears to be driven more by multi-joint movement strategy and neuromuscular coordination than by the capacity of a single muscle group.

Objectives: Compare Anterior Cruciate Ligament (ACL) Return to Sport after Injury (ACL-RSI) scores over time following ACL reconstruction (ACLR) between male and female patients aged 15 to 25 years with primary ACL injuries and ACL reinjuries. Design: Retrospective cohort design. Setting: Sports physical therapy clinics. Participants: 332 patients aged 15-25 years who underwent ACLR following either primary ACL injury or ACL reinjury, either contralateral or ipsilateral graft reinjury, and had at least one observation of the ACL-RSI. Main Outcome Measures: ACL-RSI score. Results: ACL-RSI scores significantly increased over time post- ACLR (p < .001), males reported significantly higher scores compared to females (p < .001), and patients with contralateral ACL reinjury demonstrated higher scores than those with ipsilateral ACL graft reinjury (p = .006), though there was no difference in scores between patients with primary ACL injury and ACL reinjury. A significant interaction effect of sex and injury status was also observed (p = .009), generally demonstrating that females had lower psychological readiness compared to males across injury statuses. Conclusions: ACL-RSI following ACLR varies based on biological sex and time post-ACLR, though ACL reinjury, independent of the reinjured leg, does not appear to effect scores compared to primary ACL injury.