Search Results (1,967)

Background/Objectives: Stroke is a leading cause of long-term disability worldwide and is frequently associated with reduced cardiorespiratory fitness, impaired functional capacity, and cognitive decline. High-intensity interval training (HIIT) has emerged as a promising rehabilitation strategy; however, its effects across multiple domains remain unclear. This systematic review and meta-analysis aimed to evaluate the effects of HIIT on cardiorespiratory fitness, cognitive function, and functional capacity in adults with stroke. Methods: A systematic search was conducted in PubMed, Scopus, CINAHL, and Web of Science up to April 2026. Randomized controlled trials involving HIIT interventions in adults with stroke were included. Outcomes of interest were cardiorespiratory fitness (VO₂peak), functional capacity (gait speed, walking distance, mobility, and balance), and cognitive function. Pooled effect sizes were calculated using a random-effects model. Methodological quality was assessed using the PEDro scale, and risk of bias was evaluated with the Cochrane RoB-2 tool. Results: In total, 17 studies (n = 809 participants) were included, with 12 contributing to the meta-analysis. HIIT significantly improved cardiorespiratory fitness (SMD = −0.849; p = 0.005), gait speed (SMD = 0.693; p = 0.014), walking distance (SMD = 0.604; p < 0.001), functional mobility (SMD = −0.711; p = 0.027), balance (SMD = 2.447; p = 0.002), and cognitive function (SMD = 1.741; p < 0.001). However, substantial heterogeneity was observed across most outcomes. Conclusions: HIIT appears to be an effective intervention for improving cardiorespiratory fitness, functional capacity, and cognitive performance in individuals with stroke. Nevertheless, the variability across studies suggests that its effectiveness is context-dependent. Further research is needed to standardize protocols and determine optimal implementation strategies. Full article

Background and Objectives: Sarcopenia is highly prevalent among maintenance hemodialysis (HD) patients, particularly in the presence of diabetes mellitus (DM). Dietary glycemic and insulinemic characteristics may contribute to metabolic disturbances associated with muscle deterioration, although evidence in HD populations remains limited. This study aimed to investigate the associations between dietary indices, sarcopenia, nutritional status, and clinical outcomes in diabetic (DM+) and non-diabetic (DM−) HD patients. Materials and Methods: This cross-sectional study included 92 maintenance HD patients (43 DM+ and 49 DM−). Dietary intake was assessed using three-day food records, and dietary insulin index (DII), dietary insulin load (DIL), dietary glycemic index (DGI), and dietary glycemic load (DGL) were calculated. Sarcopenia was evaluated using handgrip strength, bioelectrical impedance analysis, gait speed, and SARC-F. Anthropometric, biochemical, nutritional, and sarcopenia-related parameters were compared across tertiles of dietary indices. Results: Sarcopenia was identified in 32.6% of patients with diabetes and 36.7% of those without diabetes. Diabetic patients exhibited significantly lower handgrip strength, slower walking speed, longer walking time, and higher SARC-F scores (p < 0.01). Across DGL tertiles in DM+ patients, significant progressive increases were observed in body weight (p < 0.05), body mass index (p < 0.05), lean mass (p < 0.05), mid-upper arm circumference (p < 0.01), and triceps skinfold thickness (p < 0.01). Higher DIL and DGL tertiles were also associated with elevated serum phosphorus, LDL cholesterol, triglycerides, and total cholesterol levels (p < 0.05). DIL and DGL showed stronger associations with overall energy and nutrient intake compared with DII and DGI. However, no significant associations were identified between dietary indices and sarcopenia diagnosis or sarcopenia-related risk indicators after adjustment for age and sex. Conclusions: Dietary indices were associated with various anthropometric, biochemical, and nutritional parameters in HD patients, with more pronounced associations observed among patients with DM, suggesting a potential role of dietary quality in the nutritional and metabolic profile of this population. Full article

Cardiotonic steroids (CTS) can modulate central nervous system function through their interaction with the Na⁺,K⁺-ATPase, affecting dopaminergic transmission. While the CTS ouabain is known to induce mania-like behavior and oxidative damage, the effects of other CTS are less clear. This study examined the effects of 14-day intracerebroventricular administration of 1.5 μL 100 μM marinobufagenin (MBG) on locomotion, gait, monoamine metabolism, and oxidative stress markers (MDA, SOD, catalase, MAO-B) in C57BL/6 mice. Chronic MBG caused increased locomotor activity and time spent in the center of the open field. Unlike ouabain, chronic MBG did not impair motor function, evaluated via gait analysis. MBG elevated striatal MAO-B activity and reduced prefrontal MDA levels, with no changes in SOD or catalase, indicating that it did not cause oxidative stress. However, it did affect dopamine and serotonin metabolism. Monoamine tissue content evaluation on day 15 showed increased dopamine turnover in the striatum and brain stem, and a decrease in the thalamus. Norepinephrine levels increased in the striatum and hippocampus. Serotonin turnover increased in the prefrontal cortex. These results indicate that chronic MBG increases locomotion and reduces anxiety-like behavior through region-specific modulation of dopaminergic and serotonergic signaling distinct from that caused by ouabain. Full article

Objective: To evaluate the feasibility of adding antigravity treadmill training (ATT) or harness-based body-weight-supported treadmill training (BWSTT) to standard inpatient rehabilitation after primary hip or knee arthroplasty and to explore preliminary effects on osteoarthritis-related outcomes, balance, and psychological status. Methods: In this single-center, assessor-blinded pilot randomized trial, 60 adults within 3 months after primary hip or knee arthroplasty for osteoarthritis were allocated 1:1:1 to ATT, BWSTT, or standard inpatient rehabilitation over 6 weeks. Feasibility outcomes included recruitment, retention, and adherence. ATT and BWSTT additionally included unloading-based treadmill gait training using lower-body positive pressure or a harness system. Exploratory clinical outcomes included WOMAC total and subscale scores, analyzed using baseline-adjusted ANCOVA estimated marginal means. Secondary exploratory outcomes were BBS, FES-I, PHQ-9, and PSS-10. Results: Post-intervention data were available for 47 participants, with differential attrition across groups. Exploratory ANCOVA suggested between-group differences for WOMAC total (p = 0.004) and WOMAC function (p < 0.001). Compared with standard rehabilitation, ATT showed lower adjusted WOMAC total and function scores (both p < 0.01). ATT versus BWSTT contrasts for WOMAC total and function were statistically significant in the primary exploratory model but attenuated after hypertension adjustment. Exploratory signals were also observed for BBS and FES-I, although FES-I was less robust in sensitivity analysis. No clear between-group differences were observed for WOMAC pain, stiffness, PHQ-9, or PSS-10. No formal multiplicity adjustment was applied across exploratory endpoints. Conclusions: In this single-center pilot randomized trial, ATT suggested preliminary function- and balance-related signals that require confirmation in adequately powered multicenter trials. Full article

Background/Objectives: Artificial intelligence (AI) offers a promising approach for detecting and classifying symptom severity in patients with Parkinson’s disease (PD). The objective was to provide an overview of AI methods performance used for this classification through a systematic review and meta-analysis conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Methods: The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were searched for the period 2015–2025. The studies included were original, peer-reviewed studies written in English that addressed an AI method based on machine learning (ML) or deep learning (DL) for the classification of PD patients. The dataset used had to be “Gait in Parkinson’s Disease,” in which the severity of disease symptoms was assessed using the Hoehn and Yahr (H&Y) scale. Studies had to report at least one of the five performance metrics: accuracy, sensitivity, specificity, precision, and F1 score. Two reviewers independently selected articles, assessed the risk of bias using PROBAST (Prediction Model Study Risk of Bias Assessment Tool), and extracted data. The logit-transformed values were pooled separately by performance metrics and by severity level using a random-effects model. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were used to assess heterogeneity. Forest plots with 95% confidence intervals were used to present the results. Possible causes of heterogeneity were explored using a subgroup analysis (ML vs. DL) and a sensitivity analysis. Finally, publication bias (Egger’s test) and the certainty of the evidence (using GRADE—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to verify the generalizability of the results. Results: Among the 257 unique records, 12 studies were included. The methods demonstrated very high overall performance (>92%): accuracy (96.4%, 95% CI: 95.9–96.9%), specificity (97.7%, 95% CI: 97.3–98.1%), sensitivity (94.0%, 95% CI: 92.7–95.2%), precision (93.4%, 95% CI: 92.0–94.6%), F1 score (92.1%, 95% CI: 90.6–93.4%). Accuracy, specificity, and precision were high for all H&Y levels. However, the more advanced the symptoms, the lower the sensitivity (97.3% for H&Y0 vs. 92.1% for H&Y3). ML models achieved the best results for classifying healthy patients (H&Y0: 95.7% to 98.2%), while DL approaches performed better for classifying higher severity levels (>92%). Heterogeneity and inter-study variability were moderate (I²: 40–50% and τ²: 0.3–0.4) for precision and F1 score, and high (I² > 90% and τ² > 0.6) for accuracy, specificity, and sensitivity. The GRADE analysis revealed low-quality evidence for precision and F1 score and very-low quality for accuracy, specificity, and sensitivity. Conclusions: Thus, AI-based wearable gait assessment devices show great promise in terms of aiding clinical decision-making and treatment personalization. However, further research using a rigorous methodology (PROBAST) is needed to ensure the generalizability of the results and the clinical viability of the proposed solutions. Full article

Background/Objectives: Functional vulnerability may identify older adults hospitalized with Crohn’s disease flares who are at increased risk for adverse outcomes, but its prognostic significance in this setting remains incompletely defined. We evaluated the association between administratively defined functional vulnerability, identified using administrative diagnostic codes, and short-term clinical outcomes among adults aged ≥65 years hospitalized with Crohn’s disease flares. Methods: We conducted a retrospective cohort study using the TriNetX US Collaborative Research Network through February 2026. Functional vulnerability was identified using ICD-10-CM codes for frailty, sarcopenia, cachexia, abnormal weight loss, muscle weakness, gait/mobility abnormalities, or reduced mobility within 12 months before or during the index hospitalization. Patients coded only for nonspecific weakness or fatigue were excluded from the functional vulnerability cohort. Patients underwent 1:1 propensity score matching using demographic, comorbidity, Crohn’s disease-related, medication, nutritional, and laboratory variables. The primary outcome was 30-day all-cause mortality. Results: Among 18,420 eligible patients, 2846 met criteria for functional vulnerability, and 15,574 did not. After matching, 2720 patients remained in each cohort. Functional vulnerability was associated with higher 30-day mortality (RR 1.61, 95% CI 1.21–2.14), 90-day mortality (RR 1.40, 95% CI 1.14–1.72), bowel surgery (RR 1.29, 95% CI 1.07–1.56), sepsis (RR 1.41, 95% CI 1.18–1.68), acute kidney injury (RR 1.26, 95% CI 1.10–1.44), ICU admission (RR 1.32, 95% CI 1.13–1.55), TPN use (RR 1.47, 95% CI 1.20–1.79), and 90-day readmission (RR 1.17, 95% CI 1.07–1.29). Functionally vulnerable patients also had longer hospital stays (8.9 vs. 6.7 days; mean difference 2.2 days, 95% CI 1.9–2.5). Conclusions: Administratively defined functional vulnerability identified through diagnostic coding was associated with worse short-term outcomes among older adults hospitalized with Crohn’s disease flares. Although functional vulnerability is a recognized predictor of adverse outcomes across hospitalized populations broadly, these findings quantify its prognostic significance specifically in Crohn’s disease flare hospitalizations and suggest that functional vulnerability may identify a high-risk geriatric IBD phenotype that could benefit from early multidisciplinary assessment, nutritional optimization, rehabilitation planning, and post-discharge care coordination. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor control, balance, and gait impairments that significantly elevate fall risk. Traditional gait analysis focuses on spatiotemporal parameters, while gait variability, asymmetry, and balance measures offer more sensitive indicators of PD-related motor deficits. Machine learning studies using wearable gait data frequently report high classification accuracy but lack biomechanical interpretability and methodological rigor. Using the PhysioNet Gait in Parkinson’s Disease database, 93 individuals with PD and 72 healthy controls were analyzed during level-ground walking. Key biomechanical differences were identified: stride time coefficient of variation was significantly higher in PD bilaterally (left p = 0.001; right p = 0.003); swing-phase time was significantly reduced in both limbs (left p = 0.003; right p = 0.001); anterior–posterior center of pressure (COP) variability was significantly lower in PD for both limbs (p < 0.001); and COP path symmetry index was the most prominent asymmetry marker, significantly elevated in PD relative to controls (p = 0.003). A machine-learning analysis pipeline identified HistGradientBoosting as the best-performing classifier (AUC = 0.992; accuracy = 97.6%), but leave-one-study-out evaluation exposed substantial cross-protocol heterogeneity (AUC: 0.500–1.000), indicating that the model relied partly on dataset-specific patterns and may not generalize to independent acquisition protocols. Shapley Additive Explanations (SHAP) analysis showed classification was driven by a multimodal combination of clinical severity measures and biomechanical gait features rather than wearable metrics alone. A pre-specified gait-only sensitivity analysis that excluded clinical severity variables (UPDRS, UPDRSM, Hoehn and Yahr) confirmed that biomechanical features alone retained moderate, but substantially reduced, discriminative ability (gait-only holdout AUC = 0.844), supporting the interpretation that the headline performance reflects multimodal clinical separation rather than a stand-alone wearable-gait biomarker. These findings indicate that Parkinsonian gait impairment is characterized by timing instability and constrained forward COP progression. The combination of biomechanical analysis with interpretable predictive modeling represents a structured analysis pipeline for gait-based PD assessment; however, external validation in independent cohorts and prospective testing across acquisition protocols are required before such a pipeline can be deployed as a clinically generalizable digital biomarker. Full article

Background/Objectives: White–Sutton syndrome (WHSUS; OMIM 616364) is a rare neurodevelopmental disorder caused by pathogenic variants in the POGZ gene and characterized by developmental delay, intellectual disability, speech impairment, autism spectrum features, and dysmorphic traits. Although most reported cases are sporadic, inherited forms are exceptionally rare. We describe a familial case of WHSUS involving an affected mother and two children carrying a heterozygous POGZ nonsense variant, highlighting marked intra-familial phenotypic variability and expanding the clinical spectrum of the disorder. Methods: Clinical evaluation included multidisciplinary assessments. Genetic testing was performed using clinical exome sequencing (CES) with a virtual neurodevelopmental disorder (NDD) gene panel, followed by Sanger confirmation and segregation analysis in family members. The POGZ transcript reference NM_015100.3 was used for variant nomenclature and verified with the Mutalyzer tool. CNV detection from NGS data was performed using the Alissa CNV caller (Agilent) and visualized via IGV; the Xp11.22 microduplication was confirmed by chromosomal microarray (aCGH) and parental segregation analyses. Results: CES identified the heterozygous pathogenic POGZ variant c.1522C>T (p.Arg508*) in the female proband (III₆), an infant presenting with global developmental delay, hypotonia, speech impairment, gait abnormalities, and characteristic dysmorphic features. Segregation analysis demonstrated maternal inheritance and confirmed the presence of the variant in her affected brother (III₄), who also carries a de novo 1.79 kb microduplication at Xp11.22, while the maternal grandparents tested negative, indicating a de novo origin in the mother. The mother exhibited an attenuated phenotype, including mild neuropsychiatric and gastrointestinal manifestations. The variant is predicted to undergo nonsense-mediated decay (NMD), consistent with a moderate clinical presentation; however, experimental validation was not performed. Conclusions: This report documents a rare familial occurrence of WHSUS with highly variable expressivity. Our findings broaden the phenotypic and molecular characterization of POGZ-related disorders and emphasize the importance of comprehensive segregation studies and early genomic diagnosis. While experimental data link POGZ deficiency to DNA repair defects, no longitudinal clinical studies have demonstrated increased cancer risk in WHSUS; therefore, formal malignancy screening guidelines cannot be established at present, and this issue deserves future study in larger cohorts or registries. Full article

Background: Patients undergoing open-heart surgery (OHS) are at risk of postoperative morbidity and mortality. Physical performance has been increasingly recognized as an important factor influencing postoperative outcomes. Therefore, the study aimed to investigate the associations and predictive value of physical performance on postoperative complications and duration of hospital stay. Methods: A prospective cohort study was conducted in 116 patients who were admitted to OHS. Preoperative assessment of physical performance, i.e., Short Physical Performance Battery (SPPB), Five Times Sit to Stand Test (5STS), gait speed (5 m walk test: 5MWT), Timed Up and Go (TUG), and handgrip strength. Duration of hospital stay and incidence of post-operative complications were recorded. Differences between participants with and without postoperative complications were analyzed using independent samples t-tests for continuous variables and chi-square tests for categorical variables. The associations between physical performance and postoperative outcomes were assessed using Spearman’s rank correlation coefficient. Hierarchical regression analysis was conducted to determine the predictive contribution of physical performance. Results: A total of 116 participants were submitted for OHS in two medical school hospitals; however, 108 individuals completed the pre-operative physical performance. The most common procedures were coronary artery bypass grafting and valve surgery. Fifty-one participants (47.22%) experienced postoperative complications, including five deaths, corresponding to 4.63% mortality. For the length of hospital stay analysis, five participants who died postoperatively were excluded, resulting in a final sample of 103 participants. Physical performance was significantly associated with the length of hospital stay (p < 0.05). Hierarchical regression analysis showed that the final prediction model explained 13.4% of the variance in length of hospital stay, with SPPB independently contributing an additional 6.0% to the model, followed by 5STS, 5MWT, handgrip strength, and TUG, which accounted for an additional 5.1%, 4.6%, 4.4%, and 3.7%, respectively. Conclusions: Preoperative physical performance was associated with length of hospital stay. While each measure explained a relatively small proportion of the variance in hospital stay, these assessments offer a simple, non-invasive, and clinically feasible approach to evaluating functional reserve before surgery. These findings highlight the importance of incorporating functional assessment into perioperative care to support risk stratification and guide rehabilitation strategies. Full article

Background/Objectives: Falls have devastating consequences for older adults. The Functional Ambulation and Stair Test (FAST) was developed to characterize older adult mobility and eventual fall risk. This project aimed to determine the criterion validity of the FAST assessment by comparing the relationship between FAST outcomes and existing gold-standard clinical assessments of mobility and fall risk. A secondary aim was assessing the FAST’s capacity to elicit dual-task effects in older adults. Methods: The FAST is a multi-faceted mobility assessment combining stair navigation, turning and level-ground walking; total time and time spent in each phase are the calculated outcomes. Data from 199 older adults completing the FAST, Berg Balance Scale (BBS), Timed Up and Go (TUG), and Ten Meter Walk Test (10MWT) at comfortable and fast speed were evaluated. Relationships between the FAST and clinical outcomes were evaluated with Spearman’s correlations. The FAST and TUG were assessed under single- and dual-task conditions; linear mixed models evaluated the dual-task effects for overall FAST time and each phase. Results: Spearman’s correlations between the FAST and the BBS, TUG, 10MWT comfortable and 10MWT fast were −0.65, 0.88, −0.79, and −0.83, respectively. Participants experienced an 8.6% and 13.2% dual-task cost in the FAST and TUG, respectively. The greatest dual-task cost during the FAST was in the gait initiation, walking, and wide turn phases. Conclusions: Agreement between the FAST and gold-standard clinical mobility assessments confirms the criterion validity of the FAST. Delineation of mobility phases via the FAST offers insight into specific mobility deficits. Future work is ongoing to evaluate the FAST as a fall risk assessment in older adults. Full article

Falls among older adults remain a major public health concern, yet scalable and interpretable sensing approaches for functional fall-risk stratification remain limited. This study presents a lightweight contactless framework for five-level Short Physical Performance Battery (SPPB)-aligned functional fall-risk stratification using monocular RGB video. A total of 688 community-dwelling older adults completed SPPB-aligned assessments, including balance, five-times sit-to-stand, and 3 m gait tasks. Because prospective fall-event outcomes were unavailable, supervised labels were constructed from a pre-specified SPPB-aligned functional risk index rather than observed future falls. BlazePose-based two-dimensional keypoints were extracted, normalized using pelvis-centered and height-scaled transformations, and represented as temporal skeletal trajectories. Biomechanical descriptors were fused with embeddings from the proposed Temporal Convolutional Artificial Intelligence Fall-Risk Network (TCAI-FallNet). Participant-level data partitioning was used to reduce data leakage. TCAI-FallNet achieved a macro-averaged area under the curve of 0.91 and an overall accuracy of 81.3%. The trained model had a footprint under 3 MB, and TCN inference latency was below 20 ms per sequence under workstation-based evaluation. These findings suggest that TCAI-FallNet may support contactless SPPB-aligned functional mobility risk stratification, while prospective fall-event validation remains necessary. Full article

An electroencephalogram (EEG) signals provide vital insights for stroke rehabilitation, yet analyzing these complex, high-dimensional data to detect gait anomalies remains challenging. Artificial intelligence offers a promising solution to precisely identify abnormal movements, assisting physicians in optimizing personalized treatments. This exploratory pilot study aims to evaluate multi-class deep learning frameworks for classifying eight distinct normal and abnormal motor activities in stroke patients using EEG data. EEG signals from eight stroke patients were utilized to train and evaluate a customized Convolutional Neural Network (CNN), DeepConvNet, and EEGNet. Furthermore, channel reduction configurations (32, 22, and 15 channels) were investigated to determine optimal clinical setups. In the Leave-One-Out Cross-Validation (LOOCV) evaluation involving seven patients, EEGNet attained the highest descriptive average F1-score of 0.810. Moreover, when assessed independently on an unseen patient, it achieved an F1-score of 0.915, indicating its potential in accommodating individual differences within this limited cohort. Moreover, EEGNet exhibited a low false positive rate of 0.175, minimizing false alarms. While the 32-channel setup yielded the highest consistency, reduced configurations served as hypothesis-generating for specific tasks. In conclusion, EEGNet demonstrated superior average performance in differentiating complicated gait patterns in this exploratory pilot study, underscoring its promise for real-time, non-invasive monitoring in stroke neurorehabilitation. Full article

Background: Basal ganglia calcifications (BGCs) are frequently detected on brain CT scans in older adults, but their clinical relevance for mobility and fall risk is unclear. This study investigated the association of BGCs with impaired mobility and recurrent falls. Methods: In this cross-sectional study, all consecutive patients referred to the mobility clinic of a regional teaching hospital between 2019 and 2021 were included. Mobility was assessed using the Performance-Oriented Mobility Assessment (POMA) for balance, gait and overall mobility, and the Timed Up and Go (TUG) test for functional mobility. All assessments were performed by a trained physiotherapist. Recurrent falls were defined as self-reported occurrence of more than one fall in the past 12 months. Brain CT scans were evaluated for BGCs by a trained senior radiologist and were scored by severity. Univariable and multivariable logistic regression analyses were performed, adjusting for age, sex, and history of cardiovascular events. Results: A total of 253 participants were included (median age 82 years; 58% female), of whom 31% had BGCs. Falls data were available for 246 participants, and 70% reported recurrent falls. In both univariable and multivariable analyses, there was no evidence of a statistically significant association between the presence of BGCs and impaired balance, gait, overall mobility, functional mobility, or recurrent falls. Conclusions: No evidence of a statistically significant association was found between incidentally detected BGCs and impaired mobility or recurrent falls in older adults. Further longitudinal research is needed to confirm these findings and clarify whether BGCs are clinically relevant for mobility and fall risk assessment. Full article

Background and Objectives: Robotic-assisted total knee arthroplasty (rTKA) is being increasingly used to improve surgical precision, soft-tissue balancing, and functional recovery. However, evidence comparing rTKA with conventional manual TKA (mTKA) across functional, patient-reported, neuromotor, and safety outcomes remains heterogeneous. Materials and Methods: This narrative (non-systematic) review synthesises studies evaluating functional outcomes, patient-reported outcome measures (PROMs), joint awareness, range of motion (ROM), neuromotor recovery, and complications following rTKA versus mTKA. Study inclusion was based on author judgement and data accessibility. The reviewed evidence included five randomised controlled trials, 9 retrospective studies, six prospective non-randomised studies, two meta-analyses, one cross-sectional study, and one umbrella review, covering CT-based and imageless robotic platforms, including semi-active and active systems such as MAKO, NAVIO, CORI, ROSA, ROBODOC, CUVIS Joint, SkyWalker, TSolution One, AKEC, JIANJIA, and YUANHUA. Results: rTKA consistently demonstrated outcomes comparable to mTKA in PROMs (OKS, KOOS, WOMAC, KSS), with some studies reporting modest early improvements in pain and function. Joint awareness and patient satisfaction showed the most consistent early advantages for rTKA. Early postoperative ROM and neuromotor recovery, including balance and gait symmetry, were improved with rTKA, likely due to enhanced alignment and soft-tissue balancing; however, mid- and long-term outcomes were similar. Complication rates were low and comparable, with robotic-specific issues being rare and self-limited. Conclusions: rTKA provides small but reproducible early benefits in joint awareness, neuromotor function, and patient satisfaction, without clear long-term superiority. These early advantages may translate into meaningful population-level benefits, including faster recovery and potential healthcare cost reduction. Further high-quality studies are needed to assess long-term clinical and economic outcomes. Full article