Search Results (2,921)

Older adults are vulnerable to changes in sleep with age. Poor sleep quality is associated with self-reported cognitive changes, which can occur before the onset of objective cognitive decline associated with Mild Cognitive Impairment and Alzheimer’s disease. The objective of this study was to examine associations between self-reported sleep complaints, objective sleep quality, and self-reported cognitive changes and their relations to symptoms of depression and anxiety in a group of community-dwelling older adults. Adults aged ≥ 50 without dementia (n = 45) were recruited and completed 1–2 weeks of rest-activity monitoring using a wrist-worn device, underwent a test of global cognitive functioning (Mini-Mental State Examination; MMSE), and completed questionnaires assessing insomnia (Insomnia Severity Index; ISI), subjective sleep quality (Pittsburgh Sleep Quality Index; PSQI), self-reported cognitive changes (Cognitive Function Instrument; CFI), and symptoms of depression and anxiety (Beck Depression Inventory-II; BDI-II and Generalized Anxiety Disorder 7-item assessment; GAD-7). Pearson partial correlations assessed relations among subjective and objective sleep quality, insomnia ratings, CFI ratings, and global cognition, while controlling for BDI-II and GAD-7 ratings. Exploratory analyses examined the correlation between PSQI component scores and CFI ratings and global cognition. Greater ISI (r = 0.50, p ≤ 0.001) ratings significantly correlated with higher CFI scores. PSQI total ratings and actigraphy-based measures (n = 41) did not significantly correlate with CFI scores. Exploratory PSQI subscale analyses revealed that worse subjective sleep quality (r = 0.31, p = 0.048), shorter sleep duration (r = 0.32, p = 0.04), and greater use of sleep medications (r = 0.31, p = 0.048) correlated with higher CFI scores. Poorer sleep quality due to less time spent asleep, fragmented or disturbed sleep, and requiring medications to sleep, may be associated with greater memory concerns. Alternatively, worries about cognition may deleteriously affect sleep. Subjective measures of sleep quality may be useful to identify older adults at increased risk of cognitive decline. Full article

Background/Objectives: Stroke often results in lasting upper limb deficits. Mirror visual feedback (MVF) supports motor recovery, and electromyography-triggered electrical stimulation (ES) could enhance engagement. However, the effects in healthy older adults, age-matched to typical patient cohorts, remain insufficiently understood. We tested MVF and MVF + ES using functional near-infrared spectroscopy. Methods: Seventeen right-handed older adults performed left-wrist flexion under three visual conditions: circle fixation, viewing the right hand at rest, and mirror viewing, with/without electrical stimulation to the right-wrist flexors time-locked to left-forearm electromyography. Oxygenated hemoglobin (oxy-Hb) was recorded over the bilateral inferior frontal gyrus (IFG), precentral gyrus (PrG), postcentral gyrus (PoG), supramarginal gyrus (SMG), superior parietal lobule (SPL), and supplementary motor area. Effects were assessed with linear mixed-effects models (stimulation × visual condition); pairwise comparisons of estimated marginal means used Fisher’s least significant difference. Left-forearm electromyography verified comparable effort across conditions. Results: Linear mixed-effects models revealed left-lateralized increases in oxy-Hb, most prominently under mirror viewing with stimulation. Post hoc tests showed high oxy-Hb in the left IFG, PrG, PoG, SMG, and SMA. The left EMG did not differ. Conclusions: In healthy older adults, MVF paired with EMG-triggered ES enhances frontoparietal–motor engagement beyond MVF alone, with recruitment shaped by visuo–proprioceptive congruence. These findings support mechanistic plausibility and motivate dose–response optimization and patient-focused trials testing behavioral transfer in stroke. Full article

Background and Objectives: Boosting parasympathetic activity may enhance both physical and mental functions. In this study, we introduced the Lin Nasal Airflow Reducer (L.NAR), a silicone device designed to reduce nasal airflow. This pilot and exploratory study aimed to investigate the effect of L.NAR on parasympathetic activity in adults. Materials and Methods: The test protocol consisted of two 16 min ECG sessions. In the first session, participants did not wear the L.NAR for the initial 8 min (Test 1) but wore it for the remaining 8 min (Test 2). Following a 30 min rest, the second session reversed the sequence, with participants wearing the L.NAR for the first 8 min (Test 3) and removing it for the final 8 min (Test 4). Time- and frequency-domain analyses and non-linear analyses were used to assess heart rate variability (HRV) for every 300 s moving by 10 s. Repeated measurement ANOVA was conducted to compare the means across the four tests. Results: A total of 49 participants were enrolled in the analysis, with a mean age of 40.3 ± 10.7 years. Male participants had a higher body mass index (BMI) than female participants (24.0 ± 3.3 vs. 21.3 ± 2.9 kg/m², p = 0.014). Participants in Test 3 and Test 4 had significantly lower heart rate values than those in Test 1. Participants wearing the L.NAR (Test 2 and Test 3) had significantly higher RMSSD values compared to those not using the L.NAR. Among the participants, 33 (67.3%) who wore the L.NAR showed significantly higher RMSSD levels compared to their pre-L.NAR levels during the first practice. This improvement was achieved after an average of 2.5 ± 2.9 sessions. Conclusions: In this study, we introduced a novel approach using the L.NAR to increase RMSSD, a key indicator of parasympathetic activity. Full article

Background/Objectives: We aimed to test whether home-based low-intensity interval training (LIIT) could be equally or more effective than traditional continuous walking advice (TWA) in a population hospitalized and healed from severe COVID-19. Methods: This pragmatic nonrandomized controlled trial (NCT04615390) enrolled patients admitted to intensive care units due to COVID-19 who at discharge from the hospital were given a choice between either a home-based LIIT program or TWA. The former received a structured LIIT walking (1:1 walk:rest ratio per 10 times) to be performed at a prescribed progressively increasing speed maintained with a metronome. The latter received TWA according to the guidelines (30 min or moderate intensity activity, 5 days/week). Outcome measures, collected at baseline, at the end of the 3-month training and at the 6-month follow-up, included 6 min walking distance (primary), lower limb strength, quality of life, depression and cognitive status. Results: From a total of 85 enrolled patients, 69 of them (LIIT n = 32; TWA n = 37) completed the study. Home exercise was safely executed with an 82% adherence for the LIIT group and 64% adherence for TWA. After the 3-month program, both groups significantly improved the 6MWD (LIIT: +87 m vs. TWA +42 m; p < 0.001) with a significant difference that was also maintained at follow-up (LIIT: +138 m vs. TWA +69 m; p < 0.001). No other significant between-group differences were noted. However, patients in the LIIT group significantly improved in the majority of the outcomes, while patients of TWA improved in only the primary outcome and the physical component of quality of life. Conclusions: Compared with TWA, LIIT walking was feasible, safe and associated with more favorable multidimensional recovery in COVID-19 survivors after hospitalization for severe pneumonitis. Full article

Background and Objectives: Acute exercise lowers vagal HRV, yet it is unclear who will show the largest drop and whether simple questionnaires can identify them. To test whether resting HRV complexity (Sample Entropy) predicts the magnitude of acute vagal withdrawal and whether this physiology-based marker has greater practical utility than self-report activity/sleep measures for screening and recovery decisions. Materials and Methods: In a single-arm pre–post experimental study, twenty-nine students (20.4 ± 0.5 y; 13 males, 16 females) completed one morning visit (08:00–12:00 h). After a 2 min resting ECG and a Sustained Attention to Response Task (SART), participants cycled 15 min at 0.85 × (220 − age) bpm following a 5 min 25 W warm-up. HRV was re-recorded within ~2 min and SART ~5 min post exercise. The IPAQ defined low/medium/high activity tertiles. Correlations related baseline measures to change scores. Results: RMSSD decreased by −12.93 ms [−25.71, −2.03] (p = 0.003, r = 0.60) and SDNN by −14.91 ms [−22.30, 7.66] (p = 0.011, r = 0.51). Reaction time shortened slightly (−8.77 ms [−59.33, 30.40], p = 0.35). Activity tertiles did not differ in ΔRMSSD, ΔSDNN, or ΔRT (all p > 0.10). Sample Entropy predicted autonomic change (ΔRMSSD r = 0.43, p = 0.034; ΔSDNN r = 0.59, p = 0.002), whereas the PSQI and IPAQ did not. Equivalence tests showed non-significant tertile differences were not within our predefined equivalence bounds. Conclusions: Individuals with more complex resting HRV were more likely to show a larger immediate vagal withdrawal after moderate cycling. Questionnaires did not identify these responders. Non-linear HRV may aid practical screening/monitoring, whereas self-reports alone appear insufficient. Generalizability is limited by the homogeneous young adult sample. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

To obtain deeper information on the role played by microstructure evolution with time of particle suspensions specifically used in drilling processes, two representative time scales of a bentonite suspension were proposed. On one hand, a thixotropic time, which represents how fast the microstructure of the suspensions reaches equilibrium between build-up and break-down under shear, was obtained from hysteresis loop tests. On the other hand, a representative relaxation time, which refers to the time it takes to dissipate the stresses developed in the microstructure returning to the original free-stress state after some disturbance of the microstructure, was obtained from frequency sweep tests in the linear viscoelastic region using the Generalized Maxwell Model. The ratio of the relaxation time to the thixotropic time, named the thixo-elastic parameter, was lower than unity. Therefore, bentonite suspensions reach an equilibrium state resulting from equality of break and build processes after a long time of rest, while returning very fast to their original free-stress state, enabling the microstructure to rebuild mainly through a thixotropic phenomenon, which was almost not affected by internal stresses, and which facilitates the entrapping of rock cuttings generated during drilling processes. Full article

Background: Multimodal analgesia, combining adductor canal block (ACB) and local infiltration analgesia (LIA), is commonly used for pain control after total knee arthroplasty (TKA). However, ACB alone may not fully cover the anteromedial knee, a region extensively disrupted by TKA. Recent studies suggest that blocking branches of the anterior femoral cutaneous nerve (AFCN) could enhance analgesia, but targeted AFCN blocks are technically challenging. We evaluated supra-sartorial subcutaneous infiltration (SSSI) at the femoral triangle apex as a simpler alternative to AFCN blocks. Methods: We retrospectively reviewed 19 patients undergoing TKA with a standardized multimodal analgesic protocol, including intraoperative LIA limited to posterior capsule (PC-LIA), postoperative SSSI, and delayed intermittent ACB via catheter. SSSI involved infiltrating 20 mL of 0.3% ropivacaine into the subcutaneous plane above the sartorius muscle at the level of femoral triangle apex. Pain was assessed using Numerical Rating Scale (NRS) scores at rest and during movement at 9:00 PM on postoperative day 0 (POD 0) and 9:00 AM on POD 1, with scheduled ACB doses administered at the time of NRS pain score assessments. Rescue ACB boluses were given for intolerable pain before the first scheduled dose. Results: Eleven patients (58%) required no rescue analgesia before the first scheduled ACB, maintaining NRS scores ≤ 4 at rest and with movement for a minimum of 575–785 min post-spinal anesthesia. Eight patients needed rescue ACB, with variable pain relief. Conclusions: SSSI, when combined with PC-LIA, provided clinically meaningful analgesia in 58% of our patient cohort following TKA, though the variability observed suggests limited consistency. As a practical alternative to targeted AFCN blocks, SSSI could potentially complement ACB in multimodal pain management, but its efficacy remains uncertain due to the retrospective, non-controlled study design without a comparator group. Further investigation through prospective randomized controlled trials is warranted to validate these preliminary findings. Full article

The evaporation of sessile water drops involves coupled heat and mass transfer and is influenced by temperature, relative humidity, and the nature of the surface on which the drop rests. This work investigates the possibility of using vibration to enhance evaporation kinetics. For this purpose, experiments were conducted with vertical vibration near the resonant frequency. An original experimental device was designed, including a shaker controlled by a signal generator and an amplifier, a high-speed camera, and an adapted lighting system. The amplitude–frequency relationship was first examined to select the resonance frequency. As expected, the evaporation kinetics of two drops—one with vibration at the resonance frequency and the other without vibration—demonstrate that vibration accelerates evaporation and reduces drying time by 20.6% on PTFE substrate and by 23.5% on glass substrate. Full article

This study introduces a machine learning–driven extended reality (XR) interaction framework that leverages electroencephalography (EEG) for decoding consumer intentions in immersive decision-making tasks, demonstrated through functional food purchasing within a simulated autonomous vehicle setting. Recognizing inherent limitations in traditional “Preference vs. Non-Preference” EEG paradigms for immersive product evaluation, we propose a novel and robust “Rest vs. Intention” classification approach that significantly enhances cognitive signal contrast and improves interpretability. Eight healthy adults participated in immersive XR product evaluations within a simulated autonomous driving environment using the Microsoft HoloLens 2 headset (Microsoft Corp., Redmond, WA, USA). Participants assessed 3D-rendered multivitamin supplements systematically varied in intrinsic (ingredient, origin) and extrinsic (color, formulation) attributes. Event-related potentials (ERPs) were extracted from 64-channel EEG recordings, specifically targeting five neurocognitive components: N1 (perceptual attention), P2 (stimulus salience), N2 (conflict monitoring), P3 (decision evaluation), and LPP (motivational relevance). Four ensemble classifiers (Extra Trees, LightGBM, Random Forest, XGBoost) were trained to discriminate cognitive states under both paradigms. The ‘Rest vs. Intention’ approach achieved high cross-validated classification accuracy (up to 97.3% in this sample), and area under the curve (AUC > 0.97) SHAP-based interpretability identified dominant contributions from the N1, P2, and N2 components, aligning with neurophysiological processes of attentional allocation and cognitive control. These findings provide preliminary evidence of the viability of ERP-based intention decoding within a simulated autonomous-vehicle setting. Our framework serves as an exploratory proof-of-concept foundation for future development of real-time, BCI-enabled in-transit commerce systems, while underscoring the need for larger-scale validation in authentic AV environments and raising important considerations for ethics and privacy in neuromarketing applications. Full article

Sudden Infant Death Syndrome (SIDS) is one of the leading causes of postnatal mortality, with the prone sleeping position identified as a critical risk factor. This article presents the design, implementation, and validation of a low-cost embedded system for unobtrusive, real-time monitoring of infant posture. The system acquires data from a pressure mat on which the infant rests, converting the pressure matrix into an image representing the postural imprint. A Convolutional Neural Network (CNN) has been trained to classify these images and distinguish between prone and supine positions with high accuracy. The trained model was optimized and deployed in a data acquisition and processing system (DAQ) based on the Raspberry Pi platform, enabling local and autonomous inference. To prevent false positives, the system activates a visual and audible alarm upon detection of a sustained risk position, alongside remote notifications via the MQTT protocol. The results demonstrate that the prototype is capable of reliably and continuously identifying the infant’s posture when used by people who are not technology experts. We conclude that it is feasible to develop an autonomous, accessible, and effective monitoring system that can serve as a support tool for caregivers and as a technological basis for new strategies in SIDS prevention. Full article

Background/Objectives: Diabetic cardiomyopathy (DBCM) is characterized by cardiac dysfunction in the absence of ischemic heart disease, hypertension, or valvular disease, often manifesting as heart failure with preserved ejection fraction (HFpEF). Early recognition of DBCM is clinically important, as it enables timely initiation of tailored therapies and may slow down the progression to overt heart failure with reduced ejection fraction (HFrEF). This study aimed to evaluate the diagnostic utility of advanced echocardiographic techniques—myocardial work (MW), diastolic stress echocardiography (DSTE), Cardio-Ankle Vascular Index (CAVI)—and selected serum biomarkers in identifying DBCM. Methods: In this prospective observational study with 12-month follow-up, 125 diabetic patients with preserved ejection fraction and symptoms of HF or recent HF hospitalization were enrolled. Using the Heart Failure Association Pre-test Probability of HFpEF criteria, 37 were classified as DBCM-HFpEF and 88 as diabetic controls. An additional 47 age- and sex-matched non-diabetic individuals served as controls. All participants underwent resting echocardiography (MW, GLS), DSTE, CAVI assessment, and biomarker measurement (BNP, troponin, galectin-3). Results: Compared to non-diabetics, diabetic patients had significantly higher TRVmax (2.21 vs. 2.05 m/s), LAVI (39.70 vs. 33.50 mL/m²), E/e′ (8.64 vs. 7.59), CAVI (8.51 vs. 7.82 m/s), BNP (91.50 vs. 35.10 pg/mL), and troponin (3.94 vs. 2.43 ng/mL) (all p < 0.01), while galectin-3 levels showed no significant difference between groups. Differences were more pronounced between DBCM and No-DBCM diabetic groups. Multivariate analysis identified BNP (OR 5.45), TRVmax (OR 8.56), and CAVI (OR 1.91) as independent predictors of DBCM. Conclusions: DSTE and CAVI, alongside BNP and echocardiographic parameters, may provide valuable noninvasive tools for the early detection of DBCM in diabetic patients presenting with otherwise unexplained dyspnea, potentially enabling earlier intervention and improved outcomes. This is clinically important guiding an efficient management of an increasing number of diabetic patients presented with unexplained dyspnea. Full article

Background: Although there has been an improvement in the survival rates of patients following heart transplantation, many complications, such as hypertension, continue to develop. The aim of the study was to assess the 24-hour blood pressure profile and hypertension treatment among patients after heart transplantation and comparison to the control group. Methods: A retrospective data analysis included 26 male patients post-heart transplantation and 39 male patients in the control group. During a routine visit, the following data were collected: 24-hour blood pressure monitoring, laboratory tests, and medical history. Results: Hypertension was diagnosed in 76.9% of heart transplant recipients (HTXr) and in 56.4% of the control group (Cx). During the night-time rest period, diastolic blood pressure values ≥ 70 mmHg were observed in 76.9% of HTXr (vs. 33.33% Cx, p = 0.001). The average daytime systolic/diastolic blood pressure did not differ significantly between the groups. It was also observed that the groups differed in circadian blood pressure (Chi²ML = 15.87, p < 0.001), as there were significantly more reverse dippers in HTXr than in the control group (30.8% (8) vs. 10.3% (4)). The same proportions were also noted in HTXr and the control group in terms of isolated nocturnal hypertension. Conclusions: Heart transplant recipients require a tailored approach to hypertension management, including a variety of medications and appropriate chronotherapy. Full article

Female university ice hockey players experience elevated and sustained cardiovascular stress during training and competition. There remains limited research on the “exercise dose–response” in female ice hockey players. The purpose of this study was to examine daily and weekly changes in cardiac autonomic activity across a competitive season, and to examine its association with accumulated exercise stress. Twenty-one female ice hockey players wore chest strap heart rate monitors to quantify exercise heart rate dynamics into a training load (TL) metric and time (min) performing high-intensity activity (HIA) during training and competition. Cardiac autonomic activity was expressed as both resting heart rate (RHR) and the root mean squared of successive R-R intervals (rMSSD) and was recorded immediately upon awakening each morning. The association between HRV and both TL (r = −0.420, p = 0.058) and HIA (r = −0.420, p = 0.058) was observed. The association between RHR and both TL (r = 0.109, p = 0.638) and HIA (r = 0.150, p = 0.516) was observed. rMSSD fell below the typical error for ~50% of games. In conclusion, HRV demonstrated greater sensitivity to exercise stress than RHR for quantifying the dose–response to on-ice exercise stress. Full article

Autonomic dysfunction is a key non-motor feature of Parkinson’s disease (PD) and may influence motor performance, particularly gait. While heart rate variability (HRV) has been associated with freezing of gait, its relationship with broader gait parameters remains unclear. The objective was to investigate correlations between resting-state HRV time-domain measures and spatiotemporal gait parameters during comfortable and fast walking in patients with idiopathic PD. Twenty-eight PD patients (mean age 68 ± 9 years) were evaluated at Campus Bio-Medico University Hospital. HRV was recorded at rest using the e-Sense pule™ portable sensor, including the Baevsky’s Stress Index a measure increasing with sympathetic burden. Gait parameters were assessed via the 10 m Timed Up and Go (TUG) test using the Mon4t™ smartphone app at comfortable and fast pace. Clinical data included UPDRS III, MoCA, and disease characteristics. Gait metrics significantly changed between walking conditions. HRV parameters clustered separately from gait metrics but intersected with significant correlations. Higher Stress Index values, reflecting sympathetic dominance, were associated with poorer gait performance, including prolonged transition times, shorter steps, and increased variability (p < 0.001, r = 0.57–0.61). MoCA scores inversely correlated with the Stress Index (r = −0.52, p = 0.004), linking cognitive and autonomic status. UPDRS III and MoCA were related to TUG metrics but not HRV. Time-domain HRV measures, particularly the Stress Index, are significantly associated with spatiotemporal gait features in PD, independent of gait speed. These findings suggest that impaired autonomic regulation contributes to functional mobility deficits in PD and supports the role of HRV as a biomarker in motor assessment. Full article