Search Results (7,895)

Background: Total sleep deprivation (TSD) negatively affects athletic performance by impairing mood, anaerobic performance, and decision-making in martial arts athletes. This study examined whether a 45 min nap can alleviate deficits in mood, anaerobic performance, and decision-making caused by TSD in elite Kung-Fu athletes. Methods: Twenty-four elite male Kung-Fu athletes (age: 20.67 ± 1.76 years) participated in four randomised conditions: normal sleep, without sleep deprivation + nap, total sleep deprivation (36 h awake), and TSD + nap. Mood states were assessed in terms of the POMS-f, perceptual responses, decision-making via video-based tasks, barrage test, anaerobic performance through vertical and horizontal jumps, and isometric strength. Results: Sleep deprivation significantly affected mood, with vigour dropping by 53.8% (p < 0.001), impaired physical performance, with vertical jump declining from 36.80 ± 6.47 cm to 33.23 ± 6.03 cm (p < 0.001), and reduced cognitive function, with decision accuracy declining from 24.00 ± 2.16% to 18.44 ± 2.24% (p < 0.001) and reaction time increasing from 0.58 ± 0.08 s to 0.93 ± 0.13 s (p < 0.001). Strategic napping significantly enhanced cognitive recovery, with decision accuracy increasing by 14.1% (p < 0.001) and reaction time improving by 16.1% (p < 0.001). Physical performance showed modest gains, with vertical jump height increasing by 2.4% (p < 0.05). Conclusions: Strategic 45 min naps offer superior cognitive recovery compared to physical recovery in sleep-deprived elite Kung-Fu athletes, suggesting that coaches should incorporate strategic napping to enhance decision-making abilities during competitions involving sleep deprivation. Full article

Reaction time (RT) is a key indicator of cognitive and motor processing speed, and its age-related decline has important implications for everyday activities such as driving. However, conventional Psychomotor Vigilance Tests (PVTs) assess hand responses and do not capture lower-limb reaction characteristics relevant to pedal operations. This study aimed to compare RT characteristics between younger and older adults using the foot-response version of the PVT (Foot PVT) and to examine factors associated with RT. Sleep-related variables, physical activity level (PAL), and height were analyzed, and RT distribution characteristics were evaluated. Twenty younger adults (24 ± 3 years, range: 22–29 years) and twenty-four older adults (73 ± 5 years, range: 66–84 years) performed a 10 min Foot PVT. Mean RT was significantly slower in older adults (818 ± 105 ms) than in younger adults (700 ± 73 ms) (p < 0.001), indicating an age-related delay of approximately 120 ms. Older adults showed lower skewness and kurtosis, suggesting more homogeneous and cautious responses. In younger adults, height was negatively correlated with RT (r = −0.593, p = 0.006), and multiple regression analysis identified height as a significant predictor (adjusted R² = 0.316). No significant predictors were found in older adults. In the combined sample, age and height jointly explained 37.2% of the variance in mean RT. These findings indicate that Foot PVT performance reflects both biomechanical characteristics and age-related declines in reaction speed, supporting its utility for assessing lower-limb reaction capabilities relevant to driving and aging. Full article

Background/Objectives: Healthy lifestyle behaviors are major contributors to overall health and disease prevention. This cross-sectional study aimed to assess attitude toward a healthy lifestyle and its associations with lifestyle behaviors, body mass index (BMI), excess body weight, sociodemographic characteristics, and self-rated health in adults. Methods: The Attitudes toward a Healthy Lifestyle Questionnaire was administered to 570 Croatian adults between November and December 2025. Multiple linear and binary logistic regression models were used to examine associations between lifestyle behaviors (number of daily meals, sitting time, sleep duration), smoking and e-cigarette use, alcohol consumption, BMI, self-rated health, and attitude toward a healthy lifestyle. Results: The median attitude toward a healthy lifestyle score was 52.0 (IQR = 10), corresponding to 62% of the maximum possible score. A more positive attitude toward a healthy lifestyle was associated with a higher number of daily meals (β = 0.16, p = 0.001), longer sleep duration (β = 0.17, p < 0.001), lower sitting time (β = −0.11, p = 0.010), and lower BMI (β = −0.24, p < 0.001). Smoking was negatively associated with attitude toward a healthy lifestyle (β = −0.18; p < 0.001), while e-cigarette use was associated with fewer daily meals (β = −0.10; p = 0.025). Longer sleep duration increased the odds of excellent self-rated health (OR = 1.31, p = 0.014), and a more positive attitude toward a healthy lifestyle was associated with lower odds of excess body weight (OR = 0.92, p < 0.001). Conclusions: Attitude toward a healthy lifestyle is significantly associated with lifestyle behaviors, BMI, excess body weight, and self-rated health, highlighting the importance of psychological factors in promoting sustainable healthy lifestyles. Full article

Background/Objectives: Postprandial hyperglycemia and sleep quality can influence arterial stiffness; however, the interaction between sleep quality and postprandial hyperglycemia-induced changes in arterial stiffness remains poorly elucidated. Therefore, the purpose of this study was to investigate whether sleep quality modifies postprandial changes in blood glucose levels and arterial stiffness following a 75 g oral glucose tolerance test in healthy middle-aged and older adults. Methods: In this study on 104 healthy middle-aged and older adults (50–83 years old), arterial stiffness was assessed using carotid–femoral pulse wave velocity (cfPWV) and brachial–ankle pulse wave velocity (baPWV) before and 60 min after a 75 g oral glucose tolerance test (OGTT). Poor sleep quality was defined as a Pittsburgh Sleep Quality Index score > 5.5. Results: In the 51 participants with poor sleep quality, baPWV increased significantly from baseline to 60 min after the 75 g OGTT (p < 0.01), whereas no such change was observed in the 53 subjects with good sleep quality. baPWV was significantly higher in the poor-sleep-quality group than in the good-sleep-quality group 60 min after the 75 g OGTT (p < 0.01). Moreover, baPWV and blood glucose levels 60 min after the 75 g OGTT had a positive correlation (p < 0.01, r = 0.64). Conclusions: These findings suggest that a poor sleep quality may enhance postprandial hyperglycemia-induced arterial stiffness, whereas a good sleep quality may help protect vascular function. Full article

Obstructive sleep apnea (OSA) is a common sleep disorder that impacts patient health and imposes a burden on families and healthcare systems. The diagnosis of OSA is usually performed through overnight polysomnography (PSG) in a hospital setting. In recent years, OSA detection using a single-lead electrocardiogram (ECG) has been explored. The advantage of this method is that patients can be measured in home environments. Thus, the aim of this study was to predict occurrences of sleep apnea with parameters extracted from previous single-lead ECG measurements. The parameters were the R-R interval (RRI) and R-wave amplitude (RwA). The dataset was the single-lead ECG Apnea-ECG Database, and a stacking hybrid architecture (SHA) including three gated recurrent neural network architectures (GRNNAs) and logistic regression was proposed to improve the accuracy of OSA detection. Three GRNNAs used three different recurrent neural networks: Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). The challenge of this method was in exploring how many minutes of previous RRI and RwA measurements (n minutes) have the best performance in predicting occurrences of sleep apnea in the future (h minutes). The results showed that the SHA under an n of 20 min had the best performance in predicting occurrences of sleep apnea in the following 10 min: the SHA achieved a precision of 95.79%, sensitivity of 94.74%, specificity of 97.48%, F₁-score of 95.26%, and accuracy of 96.45%. The proposed SHA was successful in predicting future sleep apnea occurrence with a single-lead ECG. Thus, this approach could be used in the development of wearable sleep monitors for the management of sleep apnea. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is a prevalent, yet underdiagnosed, disorder associated with cardiovascular and cognitive risks. While overnight polysomnography (PSG) remains the diagnostic gold standard, it is resource-intensive and impractical for large-scale rapid screening. Methods: This study extends prior work on feature extraction and binary classification using tracheal breathing sounds (TBS) and anthropometric data by introducing a meta-modeling framework that utilizes machine learning (ML) and aggregates six one-vs.-one classifiers for multi-class OSA severity prediction. We employed out-of-bag (OOB) estimation and three-fold cross-validation to assess model generalization performance. To enhance reliability, the framework incorporates conformal prediction to provide calibrated confidence sets. Results: In the three-class setting (non, mild, moderate/severe), the model achieved 76.7% test accuracy, 77.7% sensitivity, and 87.1% specificity, with strong OOB performance of 91.1% accuracy, 91.6% sensitivity, and 95.3% specificity. Three-fold confirmed stable performance across folds (mean accuracy: 77.8%; mean sensitivity: 78.6%; mean specificity: 76.4%) and conformal prediction achieved full coverage with an average set size of 2. In the four-class setting (non, mild, moderate, severe), the model achieved 76.7% test accuracy, 75% sensitivity, and 92% specificity, with OOB performance of 88.2% accuracy, 91.6% sensitivity, and 88.2% specificity. Conclusions: These findings support the potential of this non-invasive system as an efficient and rapid OSA severity assessment whilst awake, offering a scalable alternative to PSG for large-scale screening and clinical triaging. Full article

Background: South Africa faces the world’s highest HIV burden, disproportionately affecting women, alongside rising Type 2 Diabetes (T2D). Weight gain associated with preferred dolutegravir (DTG)-based antiretroviral therapy may worsen obesity and T2D risk. This process evaluation explored the implementation of a 12-month time-restricted eating (TRE) intervention for weight management in women with HIV and overweight/obesity in Khayelitsha, Cape Town. Methods: Using the RE-AIM framework, the study investigated the implementation journey. Data were collected from three groups: RCT participants, healthcare workers (n = 21), and fieldworkers (n = 3). Methods included structured informal interviews with TRE participants throughout the intervention and semi-structured in-depth interviews (IDIs) with a subset (n = 19) at 12 months. IDIs and focus group discussions were conducted with healthcare staff. Results: Implementation faced significant contextual challenges, including high food insecurity, economic constraints, and high crime levels. Cultural norms around food hospitality also posed barriers. Despite this, TRE was highly feasible and acceptable. Participants reported positive behavioural changes, establishing eating routines and consuming healthier foods. Perceived health benefits included improved appetite control, wellbeing, sleep, and weight management. Key facilitators were the intervention’s flexibility and, importantly, the non-judgmental, empathetic support from fieldworkers, which drove engagement and retention. Healthcare workers expressed willingness to integrate TRE into existing HIV counsellor-led services, and nearly all participants desired to continue TRE post-intervention. Conclusions: This process evaluation demonstrates that TRE is a contextually suitable and acceptable intervention from an implementation perspective. Its success in practice, however, depends on mitigating complex multi-level barriers through a flexible program design and high-quality, relationship-focused support integrated into existing healthcare infrastructure. Trial registration: PACTR202302484999720, 8 February 2023. Full article

Background: The common fat mass and obesity-associated (FTO) gene variant rs9939609 has been linked to elevated risk of obesity and Type 2 diabetes mellitus (T2DM). Eating and sleeping windows gained clinical interest as factors in weight maintenance and have been linked to T2DM risk. Objective: To study the association and interaction between the common FTO rs9939609 variant and eating and sleeping windows to affect T2DM risk in a large community cohort. Methods: This cross-sectional study included 12,254 adult participants. Genetic, anthropometric, and lifestyle behaviors data including eating and fasting windows were analyzed. Logistic and linear regression models, as well as chi-square tests, were applied under additive, dominant, and recessive genetic models (adjusted for age, sex, and BMI). Results: Significant associations between FTO rs9939609 x eating and sleeping window interactions were demonstrated in relation to T2DM risk. Longer eating windows and later last meal timing were associated with an increased risk for T2DM under the additive model (OR = 1.029, 95% CI = 1.002–1.055, and OR = 1.066, 95% CI = 1.012–1.122, respectively), while longer fasting windows were found to be protective under additive model (OR = 0.972, 95% CI = 0.947–0.998). Later bedtime onset was associated with an increased risk for T2DM under additive model (OR = 1.101, 95% CI = 1.005–1.220). Hours of night sleep significantly interacted with FTO rs9939609 under additive genetic model. FTO rs9939609 risk allele carriers with prolonged sleeping windows (OR = 1.137, 95% CI = 1.039–1.354) and poorer sleeping quality (OR = 1.185, 95% CI = 1.038–1.354) had increased risk of T2DM. Conclusions: Eating and fasting windows, late last meal timing, hours of night sleep, late bedtime onset, and poorer sleep quality are significantly associated with T2DM risk among FTO rs9939609 risk carriers and may reflect metabolic vulnerability associated with FTO risk alleles. These findings highlight potential behavioral modification to attenuate genetic risk and provide evidence for actionable prevention strategies in genetically predisposed populations. Full article

Background: Childhood obesity is a growing public health concern globally and is associated with a wide spectrum of comorbidities, including sleep-disordered breathing (SDB). SDB remains under-recognized in pediatric population with obesity, particularly in Middle Eastern settings, despite its significant impact on cognitive, behavioral, and metabolic health. Objectives: To assess the prevalence of SDB among children with obesity aged 6–12 years attending King Faisal University polyclinics in Saudi Arabia and to identify key demographic and anthropometric predictors, with particular emphasis on the mediating role of neck circumference. Methods: A cross-sectional study was conducted involving 130 children with obesity aged 6–12 years. Data collection included sociodemographic characteristics, anthropometric measurements (BMI percentile, neck and waist circumference), and screening for SDB using the validated Arabic version of the Pediatric Sleep Questionnaire Sleep-Related Breathing Disorder (PSQ-SRBD) scale. Logistic regression and mediation analyses were performed to examine associations and pathways between obesity parameters and SDB risk. Results: Of the 130 participants, 37.7% screened positive for SDB risk. SDB prevalence was higher among males and older children. Neck circumference and BMI percentile were independently associated with SDB risk, with neck circumference mediating the relationship between BMI and SDB. The mediation model indicated that increased BMI contributes to SDB risk both directly and indirectly through increased neck circumference. Conclusion: SDB is highly prevalent among obese Saudi children, and neck circumference is a significant mediator of risk. Incorporating SDB screening and neck circumference measurements into routine pediatric obesity care can facilitate early detection and management. These findings support the need for integrated, multidisciplinary approaches to improve pediatric health outcomes. Full article

Background: In recent years, growing attention has been paid to the lifestyle factors that influence young adults’ well-being. University students represent young adults at risk of Sedentary Behavior (SB) and mental distress. Sport Science students represent a health-conscious population, less prone to mental distress. This study aims to investigate the associations between physical activity (PA) levels, different determinants of quality of life (QoL), orthorexia nervosa (ON) symptoms, and internet use among Sport Science students living in Naples. Methods: An online survey comprising General Data (GD) and eight validated questionnaires was used to assess PA levels, mood, sleep quality, eating habits, and digital behavior in a population of university students enrolled in Sport Science courses at Parthenope University, Naples. The statistical analyses included descriptive statistics, Student’s t-test, a Mann–Whitney U Test, frequencies, chi-square tests, and a Spearman’s rank correlation. All the analyses were performed using JASP and Jamovi software. Results: We surveyed 775 students (472 M; 303 F; 22.85 ± 3.85 y; BMI 23.74 ± 3.63 kg/m²). Regarding the MET-min/week, 65% of participants reported being highly active, 28% moderately active, and 7% inactive. Poor sleep quality was reported by 20% of those surveyed. Additionally, 84% of participants declared average internet use, which positively correlated with their emotional profile and sleep quality. High PA levels were directly associated with the presence of ON symptoms in 27% of the participants, most of whom exercised in gyms. Conclusions: To our knowledge, this is the first study conducted on a study population of Sport Science University students addressing the complex and interconnected relationships between PA levels, QoL, ON symptoms, and internet use. Full article

Background: Non-motor symptoms in Parkinson’s disease (PD) consist of a wide spectrum of gastrointestinal impairment, urinary dysfunction, sleep disturbances, fatigue and psychiatric disorders. Mood disorders like anxiety and depression are linked to general well-being and overall quality of life, therefore influencing the amount and quality of restful sleep that the patients can achieve. Objectives: The aim of this study is to determine the prevalence and characteristics of anxiety and depression in PD and to identify the factors that correlate with sleep disturbances. Methods: We conducted a case–control study which included 131 PD patients and 131 controls. Descriptive data was collected, and validated scales and questionnaires regarding sleep, motor symptoms and symptoms related to anxiety and depression were administered. Patients were divided into groups by the presence or absence of sleep disorders (“bad sleepers” and “good sleepers”) and by the presence or absence of anxiety and depression. Comparative analysis was performed. Results: PD patients reported more clinically significant depression than controls and those with concomitant sleep impairment scored higher on depression- and anxiety-specific scales than their better-sleeping counterparts. Age, motor status and sleep impairment were found to be factors associated with depression in PD patients. The presence of sleep disorders was also associated with anxiety. Conclusions: Depression and anxiety are frequent in PD and are associated with comorbid sleep disturbances. Full article

Background: Twenty-four-hour movement behaviours (physical activity, sedentary time, and sleep) may be associated with adolescent cognitive performance, but evidence from Central/Eastern Europe is limited. Methods: A total of 82 Slovak adolescents (15–19 years) completed tests of IQ, attention, and visual memory. Participants wore a wrist accelerometer 24/7 for seven consecutive days (processed in GGIR v3.0–3). Moderate-to-vigorous physical activity (MVPA), total sedentary time, and sleep duration were derived from accelerometry; physical activity was also self-reported using the Physical Activity Questionnaire for Adolescents (PAQ-A). Non-parametric tests and Spearman correlations were applied; sleep × MVPA interaction models (robust HC3 standard errors) were adjusted for age and sex. Results: MVPA was low (median 32.9 min/day; 11% met ≥60 min/day), while sedentary time was high (median 652.6 min/day). Associations between movement behaviours and cognition were generally small, and no sleep × MVPA interaction effects were observed. The PAQ-A overestimated device-based MVPA (mean bias +1.68 units; 95% limits of agreement +1.10 to +2.25), with greater overestimation in girls and older adolescents. Conclusions: In this convenience sample, 24 h movement patterns were suboptimal, and their associations with cognition were modest and exploratory. Larger longitudinal studies are needed to confirm these findings. Full article

Background/Objectives: Accurate sleep stage classification is essential for evaluating sleep quality and diagnosing sleep disorders. Despite recent advances in deep learning, existing models inadequately represent complex brain dynamics, particularly the time-lag effects inherent in neural signal propagation and regional variations in cortical activation patterns. Methods: We propose the MFST-GCN, a graph-based deep learning framework that models these neurobiological phenomena through three complementary modules. The Dynamic Dual-Scale Functional Connectivity Modeling (DDFCM) module constructs time-varying adjacency matrices using Pearson correlation across 1 s and 5 s windows, capturing both transient signal transmission and sustained connectivity states. This dual-scale approach reflects the biological reality that neural information propagates with measurable delays across brain regions. The Multi-Scale Morphological Feature Extraction Network (MMFEN) employs parallel convolutional branches with varying kernel sizes to extract frequency-specific features corresponding to different EEG rhythms, addressing regional heterogeneity in neural activation. The Adaptive Spatio-Temporal Graph Convolutional Network (ASTGCN) integrates spatial and temporal features through Chebyshev graph convolutions with attention mechanisms, encoding evolving functional dependencies across sleep stages. Results: Evaluation on ISRUC-S1 and ISRUC-S3 datasets demonstrates F1-scores of 0.823 and 0.835, respectively, outperforming state-of-the-art methods. Conclusions: Ablation studies confirm that explicit time-lag modeling contributes substantially to performance gains, particularly in discriminating transitional sleep stages. Full article

Light exposure profoundly influences human emotions and physiology. Yet, adolescents spend considerable time under artificial indoor lighting. Reduced daytime light exposure delays the circadian clock, negatively affecting sleep, cognition, and mood. This pilot study examined whether 470–490 nm enhanced LED lighting modulates mood, sleep quality, and attention among 65 male Korean high school students (mean age = 15.4 years) who participated in a two-week intervention. Both groups were exposed to natural daylight, but the experimental group additionally used LED lighting enriched in the 470–490 nm wavelength range, whereas the control group used LED lighting without modified spectral characteristics. Students were exposed to the assigned lighting from 08:00 to 17:00 during regular school hours for two consecutive weeks. To evaluate the effects of the two-week intervention, pre- and post-assessments included the Beck Depression Inventory (BDI-II), the Richards–Campbell Sleep Questionnaire (RCSQ), the Epworth Sleepiness Scale (ESS), the Perceived Stress Scale (PSS), and the Frankfurter Attention Inventory (FAIR), administered twice at each assessment point. The linear mixed-effect model showed a significant time × group interaction for line errors in the first FAIR trial (F (1, 52) = 5.21, p = 0.027, η² partial = 0.09), suggesting a greater relative reduction in attentional errors in the experimental group compared with the control group. No significant effects were observed for sleep- or mood-related outcomes. These results indicate the potential relevance of wavelength-optimized lighting in educational settings where sustained attention is critical. Future studies with larger samples and longer interventions are required to confirm and extend these findings. Full article

Although particulate matter has been associated with sleep problems, the effects of PM_2.5-bound organophosphate esters (OPEs) on children’s sleep remain unclear. OPEs have neurotoxic and endocrine-disrupting effects that may disrupt sleep–wake regulation during neurodevelopment, supporting biological plausibility for sleep impacts. In this study, we quantified the individual and mixture effects of PM_2.5-bound OPEs on the sleep disorder domain. This cross-sectional study included 110,169 children aged 6–18 years from primary and secondary schools in the Pearl River Delta (PRD), China. Sleep disorders were evaluated using the validated Sleep Disturbance Scale for Children (SDSC). Elastic net and mixed effect models identified specific OPE–sleep associations, while weighted quantile sum regression evaluated mixture effects. All odds ratios indicate a change in the likelihood of sleep disorders per interquartile range (IQR) increase in OPE concentrations. The strongest individual associations were observed for TDCIPP with short sleep duration (OR = 1.56–1.61; moderate association), TEHP with short sleep duration (OR = 1.59–1.64; moderate association), and TPHP with overall sleep disorder (OR = 1.32–1.42; modest association). Combined OPE exposure was positively associated with all sleep disorder domains (ORs = 2.02–2.85; moderate-to-large associations). These results indicate that inhaling PM_2.5-bound OPE mixtures could negatively impact children’s sleep health. This emphasizes a critical developmental period and highlights the importance of public health concerns related to emerging airborne contaminants. Full article