Search Results (1,257)

Background/Objectives: The aim of this study was to evaluate baseline health measurements among transitional housing residents (n = 29) participating in an 8-week pilot wellness intervention. Methods: Researchers measured anthropometrics, body composition, muscular strength, cardiovascular indicators, physical activity, diet quality, and health-related perceptions. Researchers analyzed data using descriptive statistics and conventional content analysis. Results: Most participants were male, White, and food insecure. Mean BMI (31.8 ± 8.6 kg/m²), waist-to-hip ratio (1.0 ± 0.1 males, 0.9 ± 0.1 females), body fat percentage (25.8 ± 6.1% males, 40.5 ± 9.4% females), blood pressure (131.8 ± 17.9/85.2 ± 13.3 mmHg), and daily step counts exceeded recommended levels. Absolute grip strength (77.1 ± 19.4 kg males, 53.0 ± 15.7 kg females) and perceived general health were below reference standards. The Healthy Eating Index-2020 score (39.7/100) indicated low diet quality. Common barriers to healthy eating were financial constraints (29.6%) and limited cooking/storage facilities (29.6%), as well as to exercise, physical impediments (14.8%). Conclusions: Residents living in transitional housing have less favorable body composition, diet, and grip strength measures, putting them at risk for negative health outcomes. Wellness interventions aimed at promoting improved health-related outcomes while addressing common barriers to proper diet and exercise among transitional housing residents are warranted. Full article

(1) Background: Physical inactivity is a major public health concern in modern society. Group fitness programs are widely used to promote physical activity, combining choreographed movements with various dance steps and music. This study aimed to examine the effects of Zumba and MoFit group fitness programs on body composition parameters in women. (2) Methods: The study included 98 female participants (Mean age = 27.8 ± 2.9 years), divided into three groups: E1 (n = 33), which followed the experimental Zumba program; E2 (n = 31), which followed the experimental MoFit program; and a control group (n = 34), which continued with their usual daily activities for 10 weeks. Body composition was assessed using 14 variables measured with the InBody 270 analyser. Statistical analyses included paired t-tests, MANCOVA, and ANCOVA. (3) Results: The findings confirmed the positive effects of both group fitness programs on most body composition parameters in women (p < 0.001). However, Bonferroni post hoc test results indicated that the Zumba program led to significantly greater improvements in most body composition variables compared to the MoFit program. (4) Conclusions: Both Zumba and MoFit programs were effective in reducing body fat, increasing muscle mass, total body water, and mineral content, whereas the control group did not achieve positive changes. Full article

Wheelchair-mounted assistive robotic manipulators can provide reach and grasp functions for power wheelchair users. This in-lab study evaluated a vision-guided shared control (VGS) system with twelve users completing two multi-step kitchen tasks: a drinking task and a popcorn making task. Using a mixed methods approach participants compared VGS and manual joystick control, providing performance metrics, qualitative insights, and lessons learned. Data collection included demographic questionnaires, the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and exit interviews. No significant SUS differences were found between control modes, but NASA-TLX scores revealed VGS control significantly reduced workload during the drinking task and the popcorn task. VGS control reduced operation time and improved task success but was not universally preferred. Six participants preferred VGS, five preferred manual, and one had no preference. In addition, participants expressed interest in robotic arms for daily tasks and described two main operation challenges: distinguishing wrist orientation from rotation modes and managing depth perception. They also shared perspectives on how a personal robotic arm could complement caregiver support in their home. Full article

Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness. Full article

Background/Objectives: Sudden cardiac arrest (SCA) is a major global health concern with high mortality despite advances in resuscitation techniques. Achieving return of spontaneous circulation (ROSC) represents merely the initial step in the extensive rehabilitation journey. This review highlights the critical role of structured, multidisciplinary rehabilitation following ROSC, emphasizing the necessity of integrated physiotherapy, neurocognitive therapy, and psychosocial support to enhance quality of life and societal reintegration in survivors. Methods: This narrative review analyzed peer-reviewed literature from 2020–2025, sourced from databases such as PubMed, Scopus, Web of Science, and Google Scholar. Emphasis was on clinical trials, expert guidelines (e.g., European Resuscitation Council 2021, American Heart Association 2020), and high-impact journals, with systematic thematic analysis across rehabilitation phases. Results: The review confirms rehabilitation as essential in addressing Intensive Care Unit–acquired weakness, cognitive impairment, and post-intensive care syndrome. Early rehabilitation (0–7 days post-ROSC), focusing on parameter-guided mobilization and cognitive stimulation, significantly improves functional outcomes. Structured interdisciplinary interventions encompassing cardiopulmonary, neuromuscular, and cognitive domains effectively mitigate long-term disability, facilitating return to daily activities and employment. However, access disparities and insufficient randomized controlled trials limit evidence-based standardization. Discussion: Optimal recovery after SCA necessitates early and continuous interdisciplinary engagement, tailored to individual physiological and cognitive profiles. Persistent cognitive fatigue, executive dysfunction, and emotional instability remain significant barriers, underscoring the need for holistic and sustained rehabilitative approaches. Conclusions: Comprehensive, individualized rehabilitation following cardiac arrest is not supplementary but fundamental to meaningful recovery. Emphasizing early mobilization, neurocognitive therapy, family involvement, and structured social reintegration pathways is crucial. Addressing healthcare disparities and investing in rigorous randomized trials are imperative to achieving standardized, equitable, and outcome-oriented rehabilitation services globally. Full article

In this study, four types of fall detection systems for seniors living alone using x-y scatter and Doppler range images measured from frequency-modulated continuous wave (FMCW) millimeter-wave (mmWave) sensors were introduced. Despite advancements in fall detection, existing long short-term memory (LSTM)-based approaches often struggle with effectively distinguishing falls from similar activities of daily living (ADLs) due to their uniform treatment of all time steps, potentially overlooking critical motion cues. To address this limitation, an attention mechanism has been integrated. Data was collected from seven participants, resulting in a dataset of 669 samples, including 285 falls and 384 ADLs with walking, lying, inactivity, and sitting. Four LSTM-based architectures for fall detection were proposed and evaluated: Raw-LSTM, Raw-LSTM-Attention, HOG-LSTM, and HOG-LSTM-Attention. The histogram of oriented gradient (HOG) method was used for feature extraction, while LSTM networks captured temporal dependencies. The attention mechanism further enhanced model performance by focusing on relevant input features. The Raw-LSTM model processed raw mmWave radar images through LSTM layers and dense layers for classification. The Raw-LSTM-Attention model extended Raw-LSTM with an added self-attention mechanism within the traditional attention framework. The HOG-LSTM model included an additional preprocessing step upon the RAW-LSTM model where HOG features were extracted and classified using an SVM. The HOG-LSTM-Attention model built upon the HOG-LSTM model by incorporating a self-attention mechanism to enhance the model’s ability to accurately classify activities. Evaluation metrics such as Sensitivity, Precision, Accuracy, and F1-Score were used to compare four architectural models. The results showed that the HOG-LSTM-Attention model achieved the highest performance, with an Accuracy of 95.3% and an F1-Score of 95.5%. Optimal self-attention configuration was found at a 2:64 ratio of number of attention heads to channels for keys and queries. Full article

Background: Comprehensive Geriatric Assessment (CGA) is essential for evaluating older cancer patients, but significant gaps persist in both research and clinical practice. This study aimed (I) to identify the CGA elements that most influence anti-cancer treatment decisions in older patients and (II) to explore the predictive value of CGA components for mortality. Methods: This observational study included older patients with newly diagnosed, histologically confirmed solid or hematological cancers, recruited consecutively from 2003 to 2023. Participants were followed for four years. The data collected included CGA measures of functional (Activities of Daily Living-ADL), cognitive (Mini-Mental State Examination-MMSE), and emotional (Geriatric Depression Scale-GDS) domains. Patients were categorized into frail, vulnerable, or fit groups based on Balducci’s criteria. Statistical analyses included decision tree modeling and Cox regression to identify predictors of mortality. Results: A total of 7022 patients (3222 females) were included, with a mean age of 78.3 ± 12.9 years. The key CGA factors influencing treatment decisions were ADL (first step), cohabitation status (second step), and age (last step). After four years, 21.9% patients had died. Higher GDS scores (OR 1.04, 95% CI 1.01–1.07, p = 0.04) were independently associated with survival in men and living with family members (OR 1.67, 95% CI 1.35–2.07, p < 0.001) in women. Younger patients (<77 years) showed both MMSE and GDS as significant risk factors for mortality. Conclusions: Functional capacity, cohabitation status, and GDS scores are crucial for guiding treatment decisions and predicting mortality in older cancer patients, emphasizing the need for a multidimensional geriatric assessment. Full article

d-Tagatose, a rare sugar, is recognized as a low-calorie sweetener, used in daily life. Although d-tagatose exhibits intriguing biological activities, the synthesis of its derivatives has rarely been reported. In this study, we developed a method for synthesizing 1,3,4,5-tetra-O-benzoyl-α-d-tagatopyranose through the regioselective benzoylation of d-tagatose in a single step, achieving an 88% yield on a gram scale. Additionally, 1,2,3,4,5-penta-O-benzoyl-α-d-tagatopyranose and 1,2,3,4,6-penta-O-benzoyl-α-d-tagatofuranose were synthesized in 50% yield as a 7:1 mixture. The structures of the three new benzoylated d-tagatose derivatives were confirmed by ¹H, ¹³C NMR, 2D NMR, FT-IR, and HRMS analyses. Full article

A novel framework is proposed to support the prediction of the plastic waste (PW) collection demand, route optimization, and overall management of PW from individual facilities. Based on electronic manifests from a local recycling company in Fukuoka, Japan, we developed a two-step artificial intelligence (AI)-based approach for predicting the demand for industrial PW (IPW) collection from a hospital. The daily hospital visitor numbers were introduced as a new independent variable in the IPW collection demand prediction. The stability (robustness) of each model was measured by its variance through experiments for two variable groups in four validation months. We found that introducing the visitor variables into IPW collection demand predictions was effective. A high monthly mean accuracy (85.06%) was achieved in predicting the daily IPW collection demand, which exceeded the accuracy of predictions using models without visitor records (84.44%). The stability of the Fine tree model with the highest prediction accuracy for March 2020 was 0.0466 $\mp$ 0.0174. Based on the findings of this study, we propose several strategies for waste management: enhancing prediction models, controlling visitor flows, and analyzing working patterns. This study successfully links AI techniques with a human mobility monitoring system (location data) for waste management using MATLAB. Full article

Background: People with Parkinson’s disease (PwPD) often experience sleep disturbances and reduced physical activity. Altered sleep behavior and lower daily steps have been linked to disease severity and symptom burden. Although physical activity may influence sleep, few studies have examined the relationship between sleep parameters and daily steps in PD. This scoping review aimed to review current knowledge on sleep parameters and daily steps collected concurrently in PwPD and their potential association. Methods: A systematic search was conducted in five databases, PubMed, Web of Science, Sport Discus, Cochrane Library, and Scopus. Methodological quality was assessed using a customized quality checklist developed by Zanardi and collaborators for observational studies, based on Downs and Black’s work. Results: Out of 1421 records, five studies met the eligibility criteria and were included in the review. Four studies reported wearable-based measurements of both step count and sleep parameters, while one study reported wearable-based measurements of step count and self-reported sleep measures. Two studies examined the association between sleep parameters and step count. One study did not find any correlation between sleep and step count, whereas one study reported a positive correlation between daytime sleepiness and step count. Conclusions: This review highlighted the lack of research investigating the relationship between sleep parameters and step count as an indicator of physical activity in PwPD. Findings are inconsistent with a potential positive correlation emerging between daytime sleepiness and step count. Findings also pointed toward lower step count and reduced sleep duration in PwPD, as measured with wearable devices. Full article

Background/Objectives: Although public health organizations encourage family walking, no studies have examined children’s physical activity and enjoyment during outdoor parent–child walks. This study addresses those gaps by examining children’s moderate-to-vigorous physical activity (MVPA) and enjoyment during outdoor walks with their parents, along with parental barriers and their relationship with parent’s self-efficacy and co-activity minutes. Methods: Fifty parent–child dyads (children aged 6–12 years) completed 10 min, self-paced outdoor walks while wearing waist-worn ActiGraph monitors. Parents reported perceived barriers to walking outdoors with their child and self-efficacy for supporting their child’s daily physical activity. Results: Children reported high enjoyment (mean = 5.1 on a six-point scale) and attained high physical activity intensity (71.3% of time in MVPA, 22.0% in vigorous activity, mean step count = 1200). Parents reported an average of 2.6 barriers (SD = 1.0) to walking outdoors with their child, with poor weather (70%) and lack of time (70%) reported most frequently. Each additional barrier was associated with a 1.3-point reduction in parents’ self-efficacy (p = 0.007). Two barriers (‘diverse interests between parent and child’ and ‘other parent-suggested barriers’) were significantly associated with fewer co-activity minutes per week (p < 0.001). Conclusions: Our study highlights the benefits of parent–child outdoor walking for promoting MVPA and enjoyment in children. Because perceived barriers may lower parents’ self-efficacy in supporting their child’s physical activity, addressing these barriers may be essential for the success of family-based interventions that encourage walking together outdoors. Full article

Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, which can largely reduce the risk of traffic accidents. In daily driving scenarios, lane changing is a common maneuver used to avoid unexpected obstacles such as parked vehicles or suddenly appearing pedestrians. Notably, lane-changing behavior is also widely regarded as a key evaluation criterion in driver license examinations, highlighting its practical importance in real-world driving. Motivated by this observation, this paper aims to develop an autonomous lane-changing system capable of dynamically avoiding obstacles in multi-lane traffic environments. To achieve this objective, we propose a hierarchical decision-making and control framework in which a Double Deep Q-Network (DDQN) agent operates as the high-level planner to select lane-level maneuvers, while a High-Order Control Lyapunov Function–High-Order Control Barrier Function–based Quadratic Program (HOCLF-HOCBF-QP) serves as the low-level controller to ensure safe and stable trajectory tracking under dynamic constraints. Simulation studies are used to evaluate the planning efficiency and overall collision avoidance performance of the proposed hierarchical control framework. The results demonstrate that the system is capable of autonomously executing appropriate lane-changing maneuvers to avoid multiple obstacles in complex multi-lane traffic environments. In computational cost tests, the low-level controller operates at 100 Hz with an average solve time of 0.66 ms per step, and the high-level policy operates at 5 Hz with an average solve time of 0.60 ms per step. The results demonstrate real-time capability in autonomous driving systems. Full article

Objectives: We aimed to identify clustering patterns of the device-measured physical activity (PA) levels (i.e., light PA and moderate-to-vigorous PA) and sedentary time (ST), screen time, sleep duration, and breakfast consumption of Spanish adolescents and their associations with the mode of commuting to and from schools (i.e., active and passive). Methods: A total of 151 adolescents aged 14.4 ± 0.6 years (53.64% girls) were included in this study. Participants wore an accelerometer device during seven consecutive days to measure PA levels and ST levels. Screen time, sleep duration, breakfast consumption, and the mode of commuting to and from school were self-reported by the participants. A two-step cluster analysis was performed to examine the different lifestyle behavior patterns (defined as data-driven groupings of daily behaviors identified through cluster analysis). Logistic regression models were used to determine the associations among the lifestyle behavior patterns and the mode of commuting to and from school. Results: The main characteristics of the three identified clusters were as follows: (active) high PA levels and low ST (38.4%); (inactive) high sleep duration and daily breakfast consumption, but low PA levels and high ST and screen time (37.2%); and (unhealthy) low PA levels and sleep duration, high ST and screen time, and usually skip breakfast (24.4%). No associations were found between these clusters and the mode of commuting to and from school (all, p > 0.05). Conclusions: Three different lifestyle behavior patterns were identified among Spanish adolescents, but no associations were found between these patterns and their mode of commuting to and from school. Full article

While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model. Full article

Renal function refers to the combined actions of the glomerulus and tubular system to achieve homeostasis in bodily fluids. While the glomerulus is essential in the first step of urine formation through a coordinated filtration mechanism, the tubular system carries out active mechanisms of secretion and reabsorption of solutes and proteins using specific transporters in the epithelial cells. The assessment of renal function usually focuses on glomerular function, so the tubular function is often underestimated as a fundamental part of daily clinical practice. Therefore, it is essential to properly understand the tubular physiological mechanisms and their clinical association with prevalent human pathologies. This review discusses the primary solutes handled by the kidneys, including glucose, amino acids, sodium, potassium, calcium, phosphate, citrate, magnesium and uric acid. Additionally, it emphasizes the significance of physicochemical characteristics of urine, such as pH and osmolarity. The use of a concise methodology for the comprehensive assessment of urine should be strengthened in the basic training of nephrologists when dealing with problems such as water and electrolyte balance disorders, acid-base disorders, and harmful effects of commonly used drugs such as chemotherapy, antibiotics, or diuretics to avoid isolated replacement of the solute without carrying out comprehensive approaches, which can lead to potentially severe complications. Full article