Search Results (324)

This study merged archival data from three separate experiments to investigate the typology of individuals who benefit most and least from gratitude letter writing interventions (N = 487). First, k-means clustering of pre- to post-intervention changes in affect revealed three distinct groups: Buffered, Mixed Feelings, and Backfired. The Buffered cluster comprised individuals who, on average, experienced decreases in negative affect (e.g., less frustration) but no changes in positive emotions (e.g., joyful). The Mixed Feelings cluster experienced increases in positive affect, alongside self-conscious emotions, particularly indebtedness, which became more closely aligned with uplifting emotional states following the intervention. The Backfired cluster experienced decreases in positive feelings and increases in negative affect. Next, differences in individual characteristics across clusters indicated that those in the Buffered cluster were relatively more neurotic, had higher baseline negative feelings, and lower trait gratitude. Individuals in the Mixed Feelings cluster tended to be more dispositionally grateful and seemed to invest more effort into the activity. Finally, individuals in the Backfired cluster were also relatively more grateful and had higher baseline positive affect. These findings contribute to understanding individual differences in the effectiveness of gratitude letter interventions and highlight opportunities to tailor such activities to promote personal growth. Full article

Background: Spondyloarthritis (SpA) typically develops before 40 years of age, but increasing life expectancy has led to a growing number of cases in older adults. It is well known that age at onset may influence disease presentation, comorbidities, and patient outcomes. Objectives: To assess whether age at onset influences SpA clinical presentation. Methods: We analyzed clinical, demographic, clinimetric, and imaging data in 272 SpA patients, grouped by onset age: early (≤40, n = 119), intermediate (41–59, n = 127), and late (≥60, n = 26). All patients had a minimum follow-up duration of 12 months. Their epidemiologic, clinic, and clinimetric data were collected, as well as patient-reported outcome measures (PROs) [Patient Global Assessment (PGA), Health Assessment Questionnaire (HAQ), FACIT-Fatigue (FACIT-F), SHORT-FORM 36 (SF-36), Hospital Anxiety and Depression Scale (HADS), Work Productivity and Activity Impairment Questionnaire (WPAI), CSI (Central Sensitization Inventory), and Psoriatic Arthritis Impact of Disease (PsAID) questionnaire]. In univariate analyses, differences in categorical variables across onset groups were assessed using Fisher’s exact test; for continuous variables, between-group comparisons were performed using the Mann–Whitney U test (two-tailed) or the Kruskal–Wallis test, as appropriate, with Bonferroni correction for post hoc analyses. Multivariable regression models were subsequently fitted, adjusting for sex, diagnosis, and disease duration. For binary outcomes, multivariable logistic regression models were used, while multivariable linear regression models (ANCOVA) were applied for continuous outcomes. The overall association between onset group and each outcome was formally tested using likelihood ratio tests, comparing models including the onset variable with nested models excluding it. A p-value < 0.05 was considered statistically significant. Results: Patients’ mean age was 60.0 ± 13.7 years; 55.9% of them were males; and there were 188 cases (69.1%) of psoriatic arthritis (PsA) and 84 cases (30.9%) of ankylosing spondylitis (AS). In early-onset patients, inflammatory back pain (IBP) was more frequent, whereas late-onset patients more often presented with joint swelling. A family history of SpA and psoriasis was less common in late-onset forms. Comorbidities, including osteoporosis, osteoarthritis, hypertension, hyperuricemia, and diabetes, were more prevalent in older-onset patients, resulting in a higher overall comorbidity burden in Groups 2 and 3. Patient-reported outcomes were largely similar across age groups, although work activity limitation was more pronounced in younger patients. Conclusions: Age at onset seems to influence SpA phenotypes: early-onset could favor axial involvement, while late-onset may associate with peripheral arthritis. Late-onset forms are associated with a more severe comorbidity burden, in particular for cardiovascular risk factors. Lung involvement proved to be more prevalent with respect to the general population, so it should be checked in the routinary assessment of SpA patients. These findings suggest that rheumatologists could tailor their routine assessments based on patients’ age at disease onset. Interestingly, work productivity seems more impacted in early-onset patients. All these points highlight the importance of age at disease onset in SpA, guiding toward personalized medicine in terms of follow-up, therapy, and more holistic patient management. Full article

This pilot study examined the feasibility, usability, and physiological effects of a high-intensity exercise program delivered through immersive virtual reality (IVR) in adults with Down syndrome (DS). Twenty participants (mean age: 29.85 ± 9.37 years) completed a 12-week intervention using the FitXR exergame on Meta Quest 3, with two sessions per week. Usability, safety, and personal experiences were assessed via the System Usability Scale (SUS), Simulator Sickness Questionnaire (SSQ), and Game Experience Questionnaire (GEQ), while body composition and strength were measured using bioelectrical impedance analysis and standardized tests (handgrip dynamometry, Five Sit-to-Stand Test). Results indicated excellent usability (SUS: 92.88–95.03/100), minimal cybersickness (SSQ: 2.12 → 1.98/48), and high adherence (90%). Positive experiences increased significantly, with no negative experiences reported. Lower-limb strength has been considered as a primary outcome, which has shown to improve significantly (p = 0.018; Cohen’s d = 0.89), whereas upper-limb strength and body composition changes were minimal. These findings suggest that IVR-based exercise is a safe, engaging, and feasible strategy for promoting physical activity and enhancing functional strength in adults with DS. Further controlled trials with longer duration and nutritional strategies are warranted to optimize body composition outcomes. Full article

Background/Objectives: Lower limb amputation (LLA) negatively affects the physical and psychological health of individuals, leading to a lower quality of life and sedentary lifestyle. The objective of this scoping review is to search for evidence regarding physical activity interventions in individuals with LLA, investigating improvements in specific outcomes related to quality of life and performance. Methods: PRISMA guidelines—extension for scoping reviews—were used to structure the study. The research was conducted between 26 July 2023 and 30 September 2023; it was structured by defining two PICO questions (P = amputation, I = physical exercise, O1 = quality of life, and O2 = performance) through Pubmed, Cochrane, and Pedro databases. The study included subjects with LLA of any etiology, in prosthetic or pre-prosthetic phase, practicing non-competitive physical activity. The results were then subjected to both qualitative and quantitative analysis. Results: Of the 615 studies identified, 18 were included in the review. They consisted of 6 systematic reviews (SR), 5 RCTs, 4 case–control studies, 1 case report (CR), and 2 cross-sectional (CS). Physical activity (PA) interventions were extremely heterogeneous and were, therefore, categorized into 6 modalities: surveys were the most reported strategies (57%), followed by personalized training (23%), strength training (13%), endurance training (13%), combined training (2%), and gait training (5%). Due to the heterogeneity of the studies, the variety of interventions proposed and the different outcomes registered, there is no evidence that one approach is more effective than another, while each group showed benefits on different specific outcomes. In total, five outcome categories were identified: quality of life was the most frequently analysed (42%), followed by cardiovascular fitness (20%), muscular fitness (14%), gait parameters (13%), functionality and disability (11%). Conclusions: PA represents a valuable strategy for improving performance and quality of life in individuals with LLA, offering a variety of interventions. Although there is no evidence that one strategy is better than the others, each activity has proven to be effective on specific outcomes, therefore, the choice must depend on the patient’s necessities. The preferred option should be the personalization of the training according to individual needs, coupled with long-term planning and remote monitoring. Creating meeting places and supporting occasions for sports activities could be a valid option. Further research could help to clarify the benefits of such interventions and enhance the understanding of how to optimize the management of LLA patients. Full article

Aging involves progressive changes in sensory perception, appetite regulation, and metabolic flexibility, which together affect dietary intake, nutrient adequacy, and health-related outcomes. Meanwhile, current wearable technologies allow continuous, minimally invasive monitoring of physiological and behavioral markers relevant to metabolic health, such as physical activity, sleep, heart rate variability, glycemic patterns, and so forth. However, digital nutrition approaches have largely focused on physiological signals while underutilizing the sensory dimensions of eating—taste, smell, texture, and hedonic response—that strongly drive dietary intake and adherence. This narrative review synthesizes evidence on the following: (1) age-related sensory changes and their nutritional consequences, (2) metabolic adaptation and markers of resilience in older adults, and (3) current and emerging wearable technologies applicable to nutritional personalization. Following this, we propose an integrative framework linking subjective (implicit) sensory perception and objective (explicit) wearable-derived physiological responses into adaptive feedback loops to support personalized dietary strategies for healthy aging. In this light, we discuss practical applications, technological and methodological challenges, ethical considerations, and research priorities to validate and implement sensory–physiological integrated models. Merging together sensory science and wearable monitoring has the potential to enhance adherence, preserve nutritional status, and bolster metabolic resilience in aging populations, moving nutrition from one-size-fits-all prescriptions toward dynamic, person-centered, sensory-aware interventions. Full article

Human Activity Recognition (HAR) using wearable sensors enables impactful applications in healthcare, fitness, and smart environments, but it also faces challenges related to data privacy, non-independent and identically distributed (non-IID) data, and limited computational resources on edge devices. This study proposes an efficient and personalized federated learning (PFL) framework for HAR that integrates federated training with model compression and per-client fine-tuning to address these challenges and support deployment on resource-constrained devices (RCDs). A convolutional neural network (CNN) is trained across multiple clients using FedAvg, followed by magnitude-based pruning and float16 quantization to reduce model size. While personalization and compression have previously been studied independently, their combined application for HAR remains underexplored in federated settings. Experimental results show that the global FedAvg model experiences performance degradation under non-IID conditions, which is further amplified after pruning, whereas per-client personalization substantially improves performance by adapting the model to individual user patterns. To ensure realistic evaluation, experiments are conducted using both random and temporal data splits, with the latter mitigating temporal leakage in time-series data. Personalization consistently improves performance under both settings, while quantization reduces the model footprint by approximately 50%, enabling deployment on wearable and IoT devices. Statistical analysis using paired significance tests confirms the robustness of the observed performance gains. Overall, this work demonstrates that combining lightweight model compression with personalization providing an effective and practical solution for federated HAR, balancing accuracy, efficiency, and deployment feasibility in real-world scenarios. Full article

The convergence of biometric and environmental sensing represents a transformative advancement in wearable technology, moving beyond single-parameter tracking towards a holistic, context-aware paradigm for health monitoring. This review comprehensively examines the landscape of multi-modal wearable devices that simultaneously capture physiological data, such as electrodermal activity (EDA), electrocardiogram (ECG), heart rate variability (HRV), and body temperature, alongside environmental exposures, including air quality, ambient temperature, and atmospheric pressure. We analyze the fundamental sensing technologies, data fusion methodologies, and the critical importance of contextualizing physiological signals within an individual’s environment to disambiguate health states. A detailed survey of existing commercial and research-grade devices highlights a growing, yet still limited, integration of these domains. As a central case study, we present an integrated prototype, which exemplifies this approach by fusing data from inertial, environmental, and physiological sensors to generate intuitive, composite indices for stress, fitness, and comfort, visualized via a polar graph. Finally, we discuss the significant challenges and future directions for this field, including clinical validation, data security, and power management, underscoring the potential of convergent sensing to revolutionize personalized, predictive healthcare. Full article

Background. Extensive research confirms that hormonal fluctuations during the menstrual cycle significantly influence female athletic performance, with profound implications for public health, including promoting equitable access to sports and enhancing women’s overall physical and mental well-being. Numerous scientifically validated methods are available to monitor hormonal status and menstrual cycle phases. However, our prior investigations revealed that these insights are rarely applied in practice due to the complexity and invasiveness of existing methods. This study examines the effects of hormonal fluctuations on elite female basketball players. It assesses practical, non-invasive, cost-effective, and field-applicable methods for hormonal monitoring, with a focus on cervical mucus analysis for estrogen crystallization. The goal is to optimize training, promote equity in women’s sports, and support public health strategies for female empowerment through sustained physical activity, addressing the limitations of male-centric training models. Materials and Methods. This exploratory field study employed a multifaceted approach, beginning with a comprehensive meta-analysis via literature searches on PubMed, SCOPUS, and Google Scholar to evaluate hormonal impacts on physical performance, supplemented by an expert survey of 20 sports scientists and coaches using Kendall’s concordance coefficient for reliability and an experimental phase involving 25 elite female Ukrainian basketball players assessed over three months through daily performance tests (e.g., sprints, jumps, agility drills, and shooting) integrated into six weekly training sessions, with cycle phases tracked via questionnaires, basal body temperature, and the fern leaf method for estrogen levels. Results. Performance peaked during the postmenstrual and post-ovulatory phases (e.g., a 7.5% increase in sprint time and a 5.1% improvement in running jump). It declined in the premenstrual phase (e.g., a 2.3% decrease in acceleration). The estrogen crystallization test using cervical mucus provided preliminary insights into hormonal status but was less precise than laboratory-based methods, such as LC-MS/MS, which remain impractical for routine use due to cost and complexity. The fern test and basal body temperature showed limited precision due to external factors. Conclusions. There is a critical need to develop simple, non-invasive, field-applicable devices for accurate, real-time hormonal monitoring. This will bridge the gap between research and practice, enhancing training personalization, equity in women’s fitness and sports, and public health outcomes by increasing female participation in physical activities, reducing gender-based health disparities, and fostering inclusive wellness programs. Full article

This research explores the language features used by leading consumer brands with successful marketing in their promotional messages. Coca-Cola, McDonald’s, PepsiCo, Mondelez, and Unilever were selected because they appear in Effie’s Most Effective Marketers’ Index and are active on a range of media platforms. A group of 225 marketing texts, made up of social media posts, video advertisement transcripts, and website content, was examined using a corpus-based method based on Biber’s MDA framework. The goal was to find common lexicogrammatical patterns in top consumer brands on five different dimensions. Many advertisements included personal pronouns, commands, and words that suggest possibility or necessity. The findings also show that most social media posts provided information, yet had a moderate impact on persuasion. Abstract nouns, passive voice, and formal connectors were found to make the website and press release texts the most impersonal and explicit. The research discovered that Unilever’s language was more informational and abstract, but McDonald’s language was mixed-purpose and non-abstract. Overall, the results indicate that brands use vocabulary and grammar to fit each platform, but maintain their brand identity. Thus, successful consumer brands use different lexicogrammatical patterns in various media to achieve their objectives. Full article

In recent years, healthcare is evolving to meet the needs of a growing and ageing population. To support better and more reliable care, a comprehensive and up-to-date Personal Health Record (PHR) is essential. Ideally, the PHR should contain all health-related information about an individual and be available for sharing with healthcare institutions. However, due to interoperability issues of the medical and fitness devices, most of the times, the PHR only contains the same information as the patient Electronic Health Record (EHR). This results in lack of health-related information (e.g., physical activity, working patterns) essential to address medical conditions, support prescriptions, and treatment follow-up. This paper introduces the B-Health IoT Box, a fog IoT computing framework for eHealth interoperability and data collection that enables seamless, secure integration of health and contextual data into interoperable health records. The system was deployed in real-world settings involving over 4500 users, successfully collecting and transmitting more than 1.5 million datasets. The validation shown that data was collected, harmonized, and properly stored in different eHealth platforms, enriching data from personal EHR with mobile and wearable sensors data. The solution supports real-time and near real-time data collection, fast prototyping, and secure cloud integration, offering a modular, standards-compliant gateway for digital health ecosystems. The health and health-related data is available in FHIR format enabling interoperable eHealth ecosystems, and better equality of access to health and care services. Full article

This study tests a theoretical model explaining reactions to animal abuse in terms of attitudes, norms, and moral obligation, based on research concerning pro-environmental and anti-ecological behavior, as offenses against animals have been considered environmental crimes in legal terms. The sample consisted of 624 people from the general population, aged 18 to 93 (64.1% female), randomly assigned one of three versions of the same scenario of abuse, differing in the category of animal (protected/pet/farm). Participants were requested to complete a questionnaire that included items about the observed variables (descriptive social norm) and latent variables (injunctive social norm, personal norm, moral obligation, attitude toward animals, speciesism, and reaction to animal abuse). The resulting model obtained appropriate fit indices (RMSEA = 0.054; CFI = 0.917) and a high percentage of explained variance of reaction (77%) and confirmed the expectation that moral obligation is the strongest predictor of reactions to animal abuse and activates the personal norm. Personal norm is predicted by attitudes toward animals and the injunctive social norm, which depends on the descriptive social norm. Speciesism was excluded from the model due to its negative covariance with attitudes toward animals and to provide a better-fitting model. The results are discussed in terms of how the human–animal relationship is mediated by the role played in animal categorization, not only by their characteristics, but also by the instrumentality attributed to them socially and culturally. Full article

Health monitoring systems play a crucial role in every life. In the 21st century, advanced technologies like wearable sensors have emerged and make healthcare better overall. These sensors collect massive amounts of data about our health over time in many dimensions. In this paper, our objective is to develop and evaluate a machine learning-based clinical decision support system using wearable sensor data to accurately classify users’ physiological states and activity contexts. The most accurate and effective model is for identifying wearable sensor-based physiological signal classification. However, there are serious privacy and security issues with sending raw sensor data to centralized computers. We gathered the multivariate physiological and activity data from wearable technology, including smartwatches and fitness trackers, which make up the dataset. Physiological signals, including heart rate, resting heart rate, normalized heart rate, entropy of heart rate variability, and caloric expenditure, are all included in the dataset. Lying, sitting, self-paced walking, and running at different MET(Metabolic Equivalent of Task) levels are examples of activity context labels. To secure our data, we proposed an architecture based on federated learning that helps machine learning model training across several dispersed devices without exchanging raw data. In this study, we used eight classifiers, and these are XGBoost, RF, Extra Trees, LightGBM, CatBoost, Bagging, DT, and GB. It has been observed that XGBoost performs well in comparison to the other classifiers with an accuracy of 0.94, a precision of 0.90, a recall of 0.89, an F1-score of 0.90, and an AUC-ROC of 0.98. This study demonstrates the potential of wearable sensor data, combined with machine learning, for accurately classifying activity and physiological conditions. The ML boosting family, especially XGBoost, exhibited strong generalization across diverse signal inputs and activity contexts. These results suggest that explainable, non-invasive wearable analytics can support early detection and monitoring frameworks in personalized healthcare systems. The proposed federated learning framework effectively combines privacy-aware computation and accurate classification using wearable sensor data. Full article

Cardiovascular exercise strengthens the heart and improves circulation, but most people struggle to fit regular workouts into their day. Short bursts of vigorous activity, sometimes called exercise snacks, can raise the heart rate and deliver meaningful health benefits. Accurate, real-time monitoring of cardio-exercises is essential to ensure that these workouts meet recommended intensity and rest guidelines. This paper proposes a Tiny Machine Learning (TinyML) wearable system that tracks the duration and type of common cardio-exercises in real time. A compact device containing a six-axis inertial measurement unit (IMU) is worn on the arm. The device streams accelerometer data to an on-device neural network model, which classifies exercises such as jumping jacks, squat jumps and jogging in place and resting states. The TinyML model is trained with labelled motion data and deployed on a microcontroller using quantization to meet memory and latency constraints. Preliminary tests with ten participants show that the system correctly recognizes the targeted exercises with around 95% accuracy and an average F1 score of 0.93 while maintaining inference latency below 100 ms and a memory footprint under 60 KB. By prompting users to alternate 30–60 s of high-intensity exercise with rest periods, the device can structure effective interval routines. This work demonstrates how TinyML can enable low-cost, low-power wearables for personalized cardiovascular exercise monitoring. Full article

Background/Objectives: The global aging population faces rising rates of cognitive decline and neurodegenerative disorders. This review explores how physical exercise influences brain health in aging, focusing on mechanisms, moderators, and personalized strategies to enhance cognitive resilience. Methods: A narrative review methodology was applied. Literature published between 2015 and 2025 was retrieved from PubMed, Scopus, and Web of Science using keywords and MeSH terms related to exercise, cognition, neuroplasticity, aging, and dementia. Inclusion criteria targeted peer-reviewed original studies in humans aged ≥60 years or aged animal models, examining exercise-induced cognitive or neurobiological outcomes. Results: Evidence shows that regular physical activity improves executive function, memory, and processing speed in older adults, including those with mild impairment or genetic risk (e.g., APOE ε4). Exercise promotes neuroplasticity through increased levels of BDNF, IGF-1, and irisin, and enhances brain structure and functional connectivity. It also improves glymphatic clearance and modulates inflammation and circadian rhythms. Myokines act as messengers between muscle and brain, mediating many of these effects. Cognitive benefits vary with exercise type, intensity, and individual factors such as age, sex, chronotype, and baseline fitness. Combined interventions—physical, cognitive, nutritional—show synergistic outcomes. Digital tools (e.g., tele-exercise, gamification) offer scalable ways to sustain engagement and cognitive function. Conclusions: Physical exercise is a key non-pharmacological strategy to support cognitive health in aging. It acts through diverse systemic, molecular, and neurofunctional pathways. Tailored exercise programs, informed by individual profiles and emerging technologies, hold promise for delaying or preventing cognitive decline. Full article

Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal–hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator–face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person’s breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance. Full article